Orals: Wed, 30 Apr | Room 2.24
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.
Tropical cyclones are becoming increasingly frequent and intense. In urban areas, the risks induced by strong winds can be amplified due to the alteration of urban airflow caused by complex urban structures. Understanding the impact of urban morphology and approaching wind conditions on urban wind environments is of great importance for enhancing urban resilience to climate change and mitigating the catastrophic effects of tropical cyclones. To address this, the present study employs Embedded Large Eddy Simulation (ELES) model to simulate the flow field within a realistic urban building complex, analyzing the probability density function of pedestrian-level wind (PLW) environments and the associated wind-induced risks. Results reveal that PLW conditions deteriorate significantly with increasing upstream terrain roughness, given a fixed reference wind speed under typhoon conditions. Specifically, normalized time-averaged and gust velocities at pedestrian level can reach up to 1.0 and 2.0, respectively, for an upstream terrain roughness length of 0.30 m, compared to 0.5 and 1.0 for a roughness length of 0.01 m. In contrast, building morphology shows limited influence on PLW under typhoon conditions, even when the average building height is halved. These findings offer valuable insights for climate-adaptive urban design and the development of sustainable cities capable of withstanding the impacts of tropical cyclones.
How to cite: Chu, R. and Wang, K.: Simulating the urban wind-induced risks under typhoon conditions using ELES model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4813, https://doi.org/10.5194/egusphere-egu25-4813, 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 21st 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.
Natural hazards have a significant impact on global populations causing fatalities and damage to agriculture, buildings, and infrastructure. With climate change, such hazards are expected to become more frequent and severe, especially in Alpine regions. The 10 deaths that occurred in Switzerland in the summer of 2024 illustrate these problems, in regions where the hazard estimated in the past is probably no longer relevant. This project aims to develop a WebGIS application for natural hazard risk assessment using open-source technologies and free data from Swiss platforms, introducing uncertainty in the parameters used to estimate the risk. This approach is similar to Cat-models.
The application was built with HTML, CSS, JavaScript, and plugins like Bootstrap, Leaflet, AGGrid, and Chart.js. Data was sourced from Swiss official platforms, and six methodologies were used to estimate potential damage by assessing building vulnerability, which can be adjusted based on expert opinions for specific areas. Moreover, statistical techniques were implemented to address missing building data.
Results include total damage values per year, exportable in CSV format, and exceedance probability curves shown in histograms and graphs. Different approaches are used to calculate risk, introducing different types of uncertainty depending on the type of input data and approach, e.g. the standard Swiss risk method, which provides only one risk value, is also used to generate exceedance curves. These results were consistent with those from those Swiss assessment tools. This probabilistic approach is standard in the insurance and reinsurance industries and for planners and decision-makers.
This study leverages open-source technologies, demonstrating that different models can be applied to various geographical areas depending on data availability. Future enhancements, such as a mobile app for assessing building attributes like height, construction type, and materials, would further increase the accuracy of damage estimates.
Link to figures:
https://wp.unil.ch/risk/helder-peixoto-web-gis-application-for-natural-hazards-risk-assessment-based-on-incomplete-data
How to cite: Peixoto, H., Jaboyedoff, M., and Derron, M.-H.: Web GIS Application for Natural Hazards Risk Assessment Based on Incomplete Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12473, https://doi.org/10.5194/egusphere-egu25-12473, 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.
In France, year 2022 witnessed severe drought conditions, with very low flows in rivers starting already during the spring season and widespread wildfire occurrences in summer. In recent years, similar occurrences of consecutive droughts and wildfire hazards have been observed in other climatic regions of the world, including Greece, Portugal, Canary Islands, Canada, California, Australia, etc. These hazards can induce numerous strong socioeconomic impacts in areas such as agriculture, silviculture, energy, ecology, drinking water, civil protection, tourism, etc., and form a complex system of multiple drivers and risks interacting over space and time. Both the individual and the joint probabilities of occurrence of these multiple hazards driving the risks are expected to evolve with climate change.
Characterizing the severity of such multiple hazards in probabilistic terms is challenging due to the multivariate nature of the problem, and the fact that each hazard has spatial structure and heterogeneity. In this presentation, we develop a relatively parsimonious stochastic model and estimation procedure to describe the joint space-time variability of three indices: (1) the Soil Wetness Index (SWI), used to characterize agricultural droughts (i.e. soil dryness); (2) River streamflow (Q), used to characterize hydrological droughts; (3) the Fire Weather Index (FWI), used to characterize fire-prone weather conditions. All indices are used at a monthly time step over the 1958-2023 period. SWI and FWI are derived from the SAFRAN atmospheric reanalysis and are available over Metropolitan France on a regular 8*8 km spatial grid (8597 pixels). Streamflow Q is measured at 232 streamgauging stations.
The statistical model is based on a causal diagram where we postulate that agricultural drought (SWI) is a precursor for both hydrological drought (Q) and fire-prone conditions (FWI). The space-time distribution of SWI is therefore modeled first using a dimensionality-reduction method to provide a parsimonious description of the space-time variability of SWI. The distribution of Q is then modeled conditionally on the average value taken by SWI on each river catchment, using a generalized additive model for location, scale and shape (GAMLSS) regression. Similarly, the distribution of FWI is modeled conditionally on the value taken by SWI on the same pixel with a GAMLSS regression.
Despite its simplicity, the stochastic model is shown to appropriately reproduce several key properties of the three studied hazards, in particular their joint probability of occurrence, their long-term trends and the distribution of the spatial extent or the duration of multi-hazard events. Future work will apply the model to future projections in order to estimate how these properties evolve under climate change. We finish by discussing the relevance of the proposed approach when extrapolated to extreme levels and whether or not this simple approach is adapted to other types of multiple hazards, such as heat + humidity or storm surge + flooding.
How to cite: Renard, B., Barbero, R., Goukouni, I., Vidal, J.-P., Mimeau, L., Furusho-Percot, C., García de Cortázar-Atauri, I., Aubry, M., Opitz, T., and Allard, D.: Probabilistic modeling of multiple spatial hazards: application to agricultural droughts, hydrological droughts and fire weather., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9039, https://doi.org/10.5194/egusphere-egu25-9039, 2025.
Research on compound events is crucial due to their increasing frequency and severity in a changing climate. These events, such as simultaneous heatwaves and droughts or concurrent storm surges and heavy rainfall, can lead to cascading impacts that far exceed the damage caused by individual extremes. Understanding the interactions and dependencies between multiple extreme factors is essential to accurately assess risks, improve predictive models and enhance resilience strategies.
In this context, particular attention must be paid to small headwater catchments, where there is a causal relationship between heavy rainfall and river flooding. In the following analyses, this relationship is examined using precipitation data from the Deutscher Wetterdienst (DWD)-RADKLIM and ERA5-Land reanalysis, as well as corresponding discharge gauges in Germany. The influence of different catchment characteristics, such as topography, on the relationship between precipitation and runoff is analysed. Sampling is a critical component for further analysis, particularly for determining joint probabilities of occurrence. This study utilises simultaneous time series of precipitation and runoff to achieve this objective. The maximum discharge within a specified period following an extreme precipitation event is determined, with the time lag between the determination of the maximum value playing a pivotal role. The present analyses provide a comprehensive illustration of the variations between different time intervals. The objective of this study is to demonstrate the influence of this parameter on the relationship between heavy rainfall and runoff in a catchment area, and to discuss the effects this has on the determination of the joint probability of occurrence. The joint probability of occurrence is determined using the correlation coefficient and corresponding copula functions.
How to cite: Simon, F. and Mudersbach, C.: Use of time lags for sampling combined flood and heavy rainfall events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9504, https://doi.org/10.5194/egusphere-egu25-9504, 2025.
As climate change contributes to sea level rise and storms intensifying around the world, coastal communities are becoming increasingly exposed to flood risks. Therefore, coastal flood protection and resilience is more important than ever. To achieve such protection, we must understand the benefits and drawbacks of various types of infrastructure built to insulate these communities from flooding. In this literature review, we examine and report on the current knowledge surrounding green, or natural, versus gray coastal infrastructure and its effectiveness specifically in regards to flood resilience. We also further explore and synthesize findings of the ways in which certain structures may affect, positively or negatively, other ecosystem services in these areas.
How to cite: Cicha, M., Rabelo Quadra, G., Robroek, B., and Fritz, C.: Effectiveness and trade-offs of green versus gray coastal infrastructure in flood resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18067, https://doi.org/10.5194/egusphere-egu25-18067, 2025.
In historically hot-arid climates like Ahmedabad, the urban environment amplifies thermal discomfort across seasons, with extreme heat dominating summer and a notable drop in wintertime temperatures. These seasonal contrasts highlight the need to evaluate how green infrastructure (GI) affects biophysical conditions and thermal comfort throughout the year. We specifically examine the effects of three GI interventions—green roofs, permeable pavements, and bioretention cells—that are feasible for cities with limited space availability and have been adopted as measures to reduce urban flooding. Our study investigates how these individual GIs influence the thermal responses of diverse population groups during both summer and winter, acknowledging the varied physiological and demographic sensitivities to seasonal extremes. Using high-resolution (3 meters) ENVI-met simulations for representative summer and winter days, we assess the thermal comfort of individuals of varying ages, genders, and social strata, using parameters like clothing insulation, metabolic rate, body weight, and surface area. We also account for seasonal shifts in thermal comfort definitions, where summer emphasizes mitigating heat stress and winter addresses cold exposure. Our results demonstrate significant seasonal differences in how GIs modulate microclimate and influence thermal responses, with implications for equitable urban design. By addressing seasonal and demographic variability, this study provides actionable insights for tailoring GI strategies to improve thermal comfort year-round in hot-arid urban contexts.
How to cite: Borah, A. and Bhatia, U.: Seasonal Variations in Thermal Comfort: Assessing Biophysical Impacts of Green Infrastructure in a Hot-Arid Urban Setting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-914, https://doi.org/10.5194/egusphere-egu25-914, 2025.
Flood nowcasting at detailed, fine spatiotemporal scales is crucial for the deployment of reliable warning systems, especially in built-up environments where the majority of socio-economic activity is concentrated. These environments are also characterized by significant complexities that require sufficient detail, up to street level. The derivation of flood maps for early warning systems can be organized via three main pillars: a) nowcasting of rainfall at high spatiotemporal resolution, typically obtained from weather radars; b) deployment of physics-based mechanistic simulators, typically based on 2D Shallow Water Equations; c) utilization of High-Performance Computing (HPC) facilities to handle the associated significant computational effort and make practically feasible the computational process. However, even with such infrastructure, there are still limitations mainly arising from: a) model errors, either related with the epistemic or the deep uncertainty of real-world randomness; b) the required simulation time which can still be prohibitive for the development of operational nowcasting tools, especially for large case study areas. The first limitation is addressed through impact-based approaches, in which uncertainties are compensated through the translation of the natural variables derived by the model (i.e. water depths and flow velocities) into classified hazard zones. With respect to the second limitation, surrogate modelling, and particularly the relevant Machine Learning (ML) techniques, promises a potential remedy to the high computational burden, since it enables the development of fast emulators based on the results derived by the mechanistic (accurate, yet slow) simulators. However, the high spatiotemporal variability of flood-related variables, as exhibited in detailed scales increases significantly the dimensionality of the problem, hampering the application of such techniques in real-world operational conditions. To address this, herein we explore the use of dimensionality-reduction techniques such as, Single Value Decomposition (SVD) and Principal Component Analysis (PCA), which are widely employed, for similar purposes, in the domain of data science. The feasibility of such methods is investigated via impact-based flood maps derived by a detailed mechanistic simulator in real-world conditions.
How to cite: Bellos, V., Tsoukalas, I., Kossieris, P., Costanzo, C., and Costabile, P.: Exploring the potential of dimensionality-reduction techniques for impact-based flood mapping, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12580, https://doi.org/10.5194/egusphere-egu25-12580, 2025.
The current trend of climate change has many implications in a variety of aspects that heavily impact human activities and society. These include an increase in the intensity and frequency of extreme weather events, including highly energetic storms with highly energetic winds, which can damage economic, social or health-critical structures and activities. Although the probability of structural damage to buildings is very low, the loss of functionality represents a real risk that is currently underestimated in risk management plans.
This work presents an operative methodology for estimating the impact of very strong wind on tall buildings, based on up-to-date numerical simulation techniques for environmental fluid dynamics. The methodology proposed is applied to a real case study in the framework of the Horizon Europe RISKADAPT project. A downscaling strategy is implemented to coupling a meteorological model at the regional scale (i.e. the Weather Research and Forecast model) with high-resolved numerical simulations of the type of Computational Fluid Dynamics (i.e. RANS and LES approaches). The former provides realistic information on the key atmospheric variables during an extreme event; the latter will be set up with these variables to reproduce the wind flow around and at the building with high accuracy. Hence, the output is a high-fidelity reproduction of the local wind circulation and the atmospheric load on buildings, along with the turbulent content of wind. The method is applied to the case study of the public Hospital of Cattinara (Trieste, Italy) which, due to its peculiarity, is particularly exposed to strong Bora winds, typical of the region.
This study is funded by the Horizon Europe RISKADAPT project (grant no. 101093939)
How to cite: Cintolesi, C., Ampatzidis, P., Sengupta, B., De Martin, F., Petronio, A., and Di Sabatino, S.: Numerical downscaling at very high resolution of wind extreme events on tall buildings , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20745, https://doi.org/10.5194/egusphere-egu25-20745, 2025.
The RISKADAPT project addresses the growing challenges caused by extreme weather phenomena on critical infrastructure. This study contributes to the project by assessing the hydrodynamic loads on the piers and abutments of the Polyfytos Bridge, located at the Polyfytos Lake, Greece. Specifically, it evaluates the discharge rates, water flow velocities, and water levels under present and future climate projections to understand the potential risks to this critical asset from climate-induced flooding. Climate change, through its effects on temperature and precipitation patterns, disrupts the hydrological cycle, resulting in altered river runoff regimes. The study employs hydrological and hydraulic modeling techniques to assess these impacts on critical infrastructure. A hydrological model is used to convert different precipitation scenarios into river discharges, considering present and future climate projections. Next, the hydraulic model simulation provides water flow parameters, which are the basis for estimating the risk of scour formation around the Polyfytos Bridge piers. The modeling was conducted using the HEC-RAS software. For the first phase, the study utilized extreme precipitation data with three return periods (50, 100, and 1,000 years) for present and future climates. Historical data were drawn from global extreme precipitation (GPEX) datasets, and future projections were sourced from the EURO-CORDEX dataset, encompassing 48 combinations of global circulation and regional climate models. These data were used to predict the impact of future climate scenarios on extreme discharges, with some projections indicating a decrease in extreme discharges, while others predict an increase of 48%, 46%, and 30% for the events with return periods of 50, 100, and 1,000 years, respectively. In the second phase, hydrological results were used to generate hydrographs, which served as an input for the hydraulic simulations at the Polyfytos Lake inflow. The hydraulic modeling provided key parameters, such as water depth, surface elevation, flow velocity, and discharge, essential for further scour analysis. Results indicated that the hydrodynamic loads on the bridge piers were relatively low, even under extreme flood events. Water flow velocities remained below 0.5 m/s during the 100-year flood event, suggesting a low risk of scour formation that could compromise the bridge’s stability. The analysis of future climate scenarios showed varying impacts on discharge rates, with some indicating an increase in extreme discharges. However, the conclusion was that the Polyfytos Bridge is not significantly susceptible to scour, even under the most extreme projected climate conditions.
How to cite: Skerjanec, M. and Rak, G.: Evaluating hydrodynamic loads on bridge piers: a pilot case study of the RISKADAPT project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10232, https://doi.org/10.5194/egusphere-egu25-10232, 2025.
Landslides are ubiquitous geomorphological phenomena occurring in various parts of the world, not only in mountainous regions with irregular terrain but also in areas with more moderate relief (e.g., cuesta fronts, plateau slopes, rocky coastal zones). Each year, they cause significant damage to populations and infrastructure. A large majority of landslides are triggered by precipitation.
Currently, there is a growing implementation of early warning systems for these rapid and sometimes destructive events. These tools represent a powerful alternative to mitigate human losses and reduce infrastructure damage. However, such tools rely on precise landslide data catalogs, including accurate location and timing. This information is essential to produce susceptibility maps and establish triggering thresholds. These thresholds enable the construction of destabilization scenarios to assist authorities during crises or emergencies while facilitating prediction and prevention efforts for local populations.
Unfortunately, in many cases, even when landslides are well-located, there remain significant uncertainties regarding their occurrence dates (ranging from weeks to months or years). For instance, the French national database reports that only 21% of landslides are dated to the nearest day, while 69% are dated beyond a month. These temporal limitations complicate the establishment of usable triggering thresholds and reduce the effectiveness of warning tools.
Since 2019, the French Pyrenees have experienced an increase in rainfall events associated with significant geomorphological manifestations on slopes, such as superficial landslides. These phenomena have impacted infrastructure, notably roads and tracks, causing traffic interruptions, as recently observed in the Aspe Valley. Some areas not previously identified as susceptible to landslides highlight the need to improve knowledge and prediction of these events.
This contribution presents a methodology applied to two sectors in the French Pyrenees (Pyrénées-Atlantiques and Hautes-Pyrénées) to establish triggering thresholds probabilities using a recent landslide catalog. The limited and recent temporal data availability raises questions about their relevance. To address this constraint, a strategy was developed to define probabilities associated with specific rainfall episodes and establish vigilance thresholds. These thresholds were spatially applied and coupled with landslide susceptibility maps to obtain triggering probabilities under given meteorological conditions. This methodology represents a first step toward the development of a warning tool for rainfall-induced landslides in the Pyrenees.
How to cite: Thiery, Y., Colas, B., and Jeremie, G.: Precipitation-induced landslides in data-scarce sites: challenges and applications in the French Pyrenees, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18988, https://doi.org/10.5194/egusphere-egu25-18988, 2025.
Historical impacts of hydro-climate extremes collected in existing global disaster databases are typically recorded at the country or subnational administrative level. Such coarse spatial resolution strongly masks the spatial variability of phenomena and limits the assessment of the potential underlying environmental and human drivers. Here, we develop a new global spatially explicit database of impacts of hydro-climate extremes by integrating hazard and exposure layers. We focus on fatalities and economic damage caused by heatwaves, cold waves, droughts, and floods occurred over the 1981-2019 period. Impact records following the occurrence of hydro-climate extremes are initially derived from existing disaster databases. For each reported impact we identify those grid cells, within the administrative unit under consideration, that experienced a hydro-climate hazard at the time of the recorded event. Spatiotemporal dynamics of hydro-climate hazards are derived using the flood-fill algorithm applied to ETCCDI indicators retrieved from ERA5-Land reanalysis data. This allows us to identify spatially coherent patterns of hydro-climate extreme conditions within a three-dimensional data cube (space-time). The reported impact is finally distributed across grid cells subject to hydro-climate hazard and using local GDP and population density as weights retrieved from high resolution global products. Results are confronted with independent observational and modeled assessments of hydro-climate impacts. This new database offers a unique contribution to improving the quantitative estimation of global socioeconomic vulnerabilities to hydro-climate extremes and the consequent risks associated with climate change.
How to cite: Li, S., Mistry, M. N., Li, N., Zantout, K., Messori, G., Schewe, J., Thiery, W., and Forzieri, G.: Improving the spatial mapping of historical climate impacts by integrating hazard and exposure layers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5802, https://doi.org/10.5194/egusphere-egu25-5802, 2025.
Landslides are a significant global hazard, posing serious risks to both human life and infrastructure, particularly in regions with unstable geological conditions. In Constantine, Algeria, landslides have been a persistent challenge, severely impacting urban areas and creating significant challenges for city planning and development. As a response to these challenges, the city of Ali Mendjeli was established 15 kilometers south of Constantine. This relocation was driven by two primary factors: managing the city's growth to prevent uncontrolled expansion and addressing the frequent landslides and natural disasters that rendered many homes unsafe. Ali Mendjeli was selected for its flat terrain and elevated position, making it ideal for urban development. The city was designed to accommodate displaced residents, mitigate landslide risks, and manage urban sprawl. In Constantine, areas with slopes ranging from 10%-20% (accounting for 45.46% of landslides) and 5%-10% (32.10%) were particularly vulnerable, prompting the relocation of residents to Ali Mendjeli. Since its establishment, Ali Mendjeli's population has grown rapidly, from 64,483 in 2008 to 243,214 in 2020, highlighting the demand for housing and infrastructure. The city's development illustrates how landslide risks in Constantine influenced population growth, providing a safer environment for displaced residents and accommodating a growing population. To investigate the landslide phenomena in Constantine, we conducted field observations to assess impacted areas and mitigation efforts. This was supported by secondary data, including a literature review, statistical population data, the Master Plan for the Development and Urbanism of Ali Mendjeli, and relevant legislation from the Official Journal of the Algerian Republic (SGG). The development of Ali Mendjeli serves as a case study demonstrating how geological hazards like landslides shape urban expansion. It highlights the importance of urban planning in managing these risks and highlights the role of interdisciplinary collaboration in fostering safer, more stable communities.
Keywords: Landslide Risk, Geological Hazards, Urban Planning, Urban Resilience, Population Relocation, Population Growth, Algeria.
How to cite: Saidi, I., Abdelkader, M., and Czimre, K.: Landslide Risk and Urban Development: The Response of Ali Mendjeli to Constantine's Geological Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9734, https://doi.org/10.5194/egusphere-egu25-9734, 2025.
Experiences with climate change-induced events incentivise research on prevention and management of the effects. Risk assessment is an important tool to envisage risks related to climate change and the socio-economic impacts. Therefore, insight into socio-economic impacts is crucial. In this paper a meso and micro perspective will be used to analyse the socio-economic impacts of climate change in the case of Cattinara hospital in Trieste, Italy. The meso perspective encompasses the findings of the development of a spatial microsimulation model aimed at estimating geographical distributions of relevant socio-economic indicators for regions affected by climate induced events. It also includes the use of Geographical Information Systems (GIS) to map the outputs as well as econometric analysis of the model outputs. The simulation outputs (i.e. the attributes of the synthetic individuals) can include a wide range of policy relevant variables such as earned income, employment status and sector, age, well-being measures and perceptions on various aspects of individuals’ lifes among others. The findings show that a fully operational hospital is positively and significantly linked to the happiness levels of municipalities. However, partially operational hospitals do not exhibit a statistically significant relationship with happiness when we control for municipalities’ socio economic characteristics.
The micro perspective comprises the findings of a survey distributed among technicians, practitioners of the Cattinara hospital and representatives of civil society organisations of the municipality of Trieste and others. The findings demonstrate that hospitalized people are most vulnerable and exposed to the health impacts that may be created by likely climate change-induced damage to the Cattinara hospital, followed by hospital personnel. Damage to the hospital building is the most relevant economic impact that might be created by climate change extreme events, followed by the impacts on the whole hospital’s supply chain. The impacts on the logistics associated with public services provision in relation to the likely need for transferring patients to other healthcare facilities and/or to the temporary hospital’s closure are the most relevant.
While the meso perspective on climate change impact indicates that a partically functioning hospital is important assuming that access to health care will be continued, the micro perspective on climate change impact points out that the hospital’s building and supply chain have to be taken into account as well in risk assessment of climate change-induced events.
How to cite: Winnubst, M.: Meso and micro perspective on climate change impacts, the case of Cattinara hospital Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17562, https://doi.org/10.5194/egusphere-egu25-17562, 2025.
Critical infrastructure, including transportation, energy supply, telecommunications, water supply, and government and emergency services, is essential for sustaining societal functioning and the well-being of people. Ensuring accessibility to critical facilities, such as health centers and fire stations, is particularly crucial for supporting life-saving and life-sustaining activities during and after disasters.
Flooding, a frequent and costly natural hazard, presents significant challenges to infrastructure accessibility. With climate change, the frequency and intensity of coastal and fluvial flooding are projected to increase, highlighting the need for a deeper understanding of its impacts on critical facilities. Ensuring these facilities remain accessible during and after flooding protects vulnerable populations and facilitates life-saving activities.
This study examines the impact of flood-induced disruptions on accessibility to health centers in Ennis, Ireland, under three scenarios: the Present Day, Mid-Range Future Scenario (MRFS), and High-End Future Scenario (HEFS). These scenarios reflect the anticipated increases in flood frequency and intensity for both coastal and fluvial flooding under future climate conditions. High-resolution flood maps are used to simulate the spatial extent of flooding and its effects on the road network.
A comprehensive framework is developed to assess accessibility loss and road criticality, integrating both physical and social vulnerabilities. This framework is designed to monitor the deterioration of territorial accessibility to critical infrastructure as a result of the cumulative elimination of road sections due to flooding. It incorporates a betweenness centrality (BC) metric to identify essential road segments that connect communities to critical services, helping to pinpoint areas most vulnerable to disruption. This approach enables the identification of key routes that are crucial for maintaining access to critical services during and after flooding events, enhancing preparedness and resilience. Social vulnerability is evaluated through a Social Vulnerability Index, emphasizing the disproportionate impacts on vulnerable populations, such as the elderly, children, low-income households, the disabled, and those with bad and very bad health conditions.
The results reveal significant reductions in accessibility across all scenarios, with disparities worsening under future climate conditions. In the MRFS, the frequency and extent of accessibility disruptions increase compared to the present day, with travel times to health centers rising significantly, reflecting moderate climate impacts. In the HEFS, the situation becomes more dire, with a large portion of critical roads becoming impassable, and travel times to health centers and fire stations increasing substantially in the worst-affected areas.
These findings highlight the urgent need to improve infrastructure and implement proactive planning to address access challenges caused by flooding. Recommendations include upgrading critical roads, establishing real-time flood response systems, and temporarily relocating services during extreme flood events. By integrating social vulnerability into planning, this research offers practical guidance for fostering equitable community resilience and ensuring uninterrupted access to essential services during future climate-related disruptions. Emphasizing a resilience-based approach, the study provides actionable insights for policymakers and stakeholders in Ennis and similar urban areas to develop sustainable solutions that address both the physical impacts of flooding and the associated social vulnerabilities, underscoring the critical role of climate change adaptation strategies in safeguarding critical infrastructure and protecting vulnerable populations.
How to cite: Sangeeta, S., Sarma, H. D., Martinez-Pastor, B., McHenry, H., and Teixeira, R.: Assessing Critical Facility Accessibility and Road Network Criticality Under Flood-Induced Failures: A Resilience-Based Framework for Climate Change Adaptation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14663, https://doi.org/10.5194/egusphere-egu25-14663, 2025.
Global electricity generation depends on cooling-reliant plants (78%), but rising temperatures could reduce their output. Steam-cycle air-cooling (ST-AC) technology, which is poorly adapted to high temperatures, is widely used in power plants in developing countries (40.7%) compared to developed ones (25.2%). However, few studies have evaluated the performance of different cycle-cooling technologies under heat stress. Here, we developed a Global Power Plant Dataset comprising 109,110 thermal and nuclear power units across six fuel types and seven cycle-cooling technologies, resulting in 32 distinct fuel-technology combinations. We then assessed the impact of heatwave events on these fuel-technology combinations at the plant level, and the effects of generation losses on residents under three SSP (Shared Socioeconomic Pathways) - RCP (Representative Concentration Pathways) scenarios. From 2030 to 2060, losses are expected to reach 1205.4 (±255.1) TWh under SSP5-8.5, accounting for 5.2% (±1.1%) of the annual global output of thermal and nuclear plants, which is 1.4 to 2.4 times higher than under SSP2-4.5 and SSP1-1.9. Vulnerable plants, including India’s coal-fired ST-AC Mundra plant, Congo’s gas-fired gas turbine Côte Matève plant, and Mexico’s oil-fired steam-cycle once-through cooling Lopez Mateos plant, could experience losses that put millions of residents in these regions at risk of electricity accessibility. Identifying these vulnerable plants would support developing countries' efforts to adapt their power sectors to a warming future.
How to cite: He, Y., Sun, Y., Lei, T., Guan, D., Gao, X., and Zhang, N.: Heatwaves Intensify Power Shortages in Developing Countries, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2860, https://doi.org/10.5194/egusphere-egu25-2860, 2025.
Conventionally, models used for flood inundation forecasting are typically physically based and computationally intense. This limits their suitability for operational flood inundation forecasting where high-resolution data are critical. Deep Learning (DL) models have been proven to be able to reduce the computational burden while maintaining acceptable accuracies. However, some DL surrogate models often require complex model architectures that result in high computational costs to capture flood dynamics across the entire domain.
With the recent development of advanced DL models, generative models have the potential to overcome the need for computationally expensive model architecture and to be useful in flood inundation forecasting. Generative models can: generate synthetic data, capture complex relationships between different variables (e.g., hydrological, meteorological and topographical estimates) and allow for domain transferability. In this study, we developed a deep generative model as a surrogate model for flood inundation forecasting and investigated its performance under various spatial and temporal resolutions. The initial results indicate that increasing spatial resolution has a bigger impact on model training time compared to increasing temporal resolution; however, does not impact model prediction time. Additionally, the model accuracy tends to increase with the increase in resolution at the expense of computational costs. Enlarging the computation sub-domain can shorten the overall model run time and improve model accuracy but it's subject to hardware capacity. These findings indicate that the proposed generative surrogate model has the potential for operational flood forecasting.
How to cite: Yang, C. and O'Loughlin, F.: Performance of a deep learning generative surrogate model for flood inundation forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7047, https://doi.org/10.5194/egusphere-egu25-7047, 2025.
In the last decades we have observed a rapid growth of extreme hydrological events, such as floods and rock/debris or mud flows affecting more and more frequently our lives. The detailed physical description of these viscous fluids is fundamental to understand the caused stress on possible flood control structures, such as levees, dams, check dams. However, its simulation through high fidelity physics-based computational models, using for example the Material Point Method (MPM), is extremely computationally demanding, thus limiting the application to real system monitoring.
The development of surrogate models to efficiently replicate the relevant features of the flow is of paramount importance to make a substantial step in the direction of real-time computations, required in any early warning system and to develop mitigation strategies.
Surrogate models have gained significant attention in recent years, especially with the advent of machine learning and the development of neural network-based methods, such as Fourier Neural Operators and Deep Operator Networks, among others.
Here we consider surrogates based on Kernel methods, which demonstrated distinct advantages over widely used neural network-based approaches and provide rigorous error analysis. As fractal functions are pivotal in addressing nonlinear and irregular problems, we propose using the recently developed fractal RBFs as kernel of the surrogate model.
To demonstrate the effectiveness of the proposed approach, we consider a 2D debris flow along a 5m flume as a test scenario, where the outputs of interest are the position of the front and the velocities as functions of the fluid density and the inclination angle of the slope.
Our results explore the accuracy and computational efficiency of the fractal RBF surrogate model compared to other kernel-based approaches.
How to cite: Pasetto, D., Kumar, D., Spricigo, E., Putti, M., and Larese, A.: An RBF Approach for Enhanced Surrogate Modeling of a Debris Flow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16747, https://doi.org/10.5194/egusphere-egu25-16747, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot 2
EGU25-834 | ECS | Posters virtual | VPS29
Disproportionate Impact of Compound Flood Events on Road Infrastructure DamageThu, 01 May, 14:00–15:45 (CEST) | vP2.17
The resilience of road infrastructure is vital for maintaining community mobility and ensuring the continuity of critical services, particularly in the face of escalating challenges posed by climate change. Among these challenges, the increasing frequency and intensity of extreme weather events often manifest as floods, posing a substantial threat to urban road networks in low-lying coastal areas. These regions are especially vulnerable to multiple flood drivers, including tidal surges, streamflow, and precipitation. The co-occurrence of extreme rainfall with high tides and elevated streamflow levels amplifies flood inundation depths, yet the compound effects of these flood drivers on road infrastructure damage remain underexplored. This study proposes a quantitative framework to assess the dynamic interaction of compound flood events and their impacts on road infrastructure systems, with a focus on damage assessment. Using the extreme weather events of 2018 in Kozhikode, Kerala, India, as a case study, we integrate disparate flood hazards—pluvial (rainfall-induced), fluvial (streamflow), and coastal (storm tide)—to evaluate flood risk and road damage. A 1D-2D hydrodynamic modeling approach, coupled with depth-damage curves, quantifies the repair and maintenance costs for roads affected by compound flooding. Our findings reveal that pluvial flooding accounts for 93% of road damage, while fluvial and coastal flooding contribute 5.6% and 1.4%, respectively. This framework highlights the disproportionate impacts of different flood drivers and enables the identification of the primary contributors to road damage. Such insights can inform targeted adaptation strategies tailored to the unique needs of specific regions, enhancing infrastructure resilience against future flood events.
How to cite: Dave, R., Sen, S., and Bhatia, U.: Disproportionate Impact of Compound Flood Events on Road Infrastructure Damage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-834, https://doi.org/10.5194/egusphere-egu25-834, 2025.
EGU25-867 | ECS | Posters virtual | VPS29
Quantifying Subsurface Contributions to Compound Flooding in Coastal Urban Areas for Enhanced ResilienceThu, 01 May, 14:00–15:45 (CEST) | vP2.18
Coastal cities are increasingly affected by flooding due to the combined impacts of surface and subsurface water processes. Compound flood events, driven by changing groundwater levels, tidal surges, and riverine, pose substantial risks to urban infrastructure and livelihoods, particularly in low-lying coastal cities like Mumbai. Despite its critical importance, the role of groundwater dynamics in flood severity and its contribution to comprehensive flood risk assessments remain underexplored. In this study, we quantify flood risks induced by multiple drivers and their contributions to compound events. We integrate surface and subsurface flooding using MIKE+ and FEFLOW to simulate 1D-2D coupled hydrodynamic models, respectively. Mumbai serves as the study area due to its susceptibility to tidal surges, riverine, and groundwater flooding. By incorporating tidal, well, and streamflow data, our study quantifies the contribution of groundwater to surface flooding, offering a deeper understanding of the interplay between subsurface and surface water processes. Our findings lay the foundation for proactive groundwater management strategies and promote the development of resilient urban infrastructure, ultimately mitigating the impacts of flooding in vulnerable coastal areas.
How to cite: Sutrave, M., Kumar, A., Dave, R., and Bhatia, U.: Quantifying Subsurface Contributions to Compound Flooding in Coastal Urban Areas for Enhanced Resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-867, https://doi.org/10.5194/egusphere-egu25-867, 2025.
