Orals: Tue, 29 Apr | Room N2
Levees are critical adaptation measures for mitigating the escalating flood risks posed by intensifying climatic extremes and rapid urban expansion into flood-prone areas. However, the implementation of these measures is often constrained by administrative boundaries and financial limitations, which confine adaptation efforts to predefined jurisdictions. These constraints result in adaptation gaps that disproportionately affect communities beyond protected zones and exacerbate inequalities in flood risk distribution. Our study integrates hydrodynamic and economic modeling to evaluate the magnitude, spatial distribution, and economic losses associated with levee-based flood protection strategies in Surat, a coastal city frequently exposed to severe riverine flooding. The findings indicate that levees designed to safeguard administrative boundaries can inadvertently intensify flood risks for unprotected communities and infrastructure by altering hydrodynamic conditions. Specifically, 100-year flood damages and extents increase significantly due to levee-induced changes. Our preliminary results highlight the spatially widespread nature of flood impacts, unaccounted costs, and the potential for increased socioeconomic inequities. These findings emphasize the need for a systems-based approach to flood management that considers the interconnectedness of river systems and promotes equitable sharing of flood risks across jurisdictions.
How to cite: Kumar, A. and Bhatia, U.: Flood risk redistribution due to gaps and constraints in adaptation strategies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15089, https://doi.org/10.5194/egusphere-egu25-15089, 2025.
In the last decade, there has been an increase in flood damages driven by increased land use pressures and climate change impacts. Remote sensing solutions for the rapid estimation of damage cause to property are in high demand by the insurance sector. Such solutions would also enable the rapid estimation of the number of affected properties, this reducing the costs associated with loss adjustment and on-site inspections. This contribution presents a novel approach based on Unmanned Aircraft System (UAS) as part of a loss-adjustment framework for the estimation of direct tangible losses to residential properties affected by flooding. The specific case of the floods after storm Desmond (5 and 6 December 2015) over Cockermouth (Cumbria, UK) is used for that purpose. The proposed framework overcomes some of the limitations associated with traditional remote sensing methods such as low-cloud cover presence, oblique viewing angles, and the resolution of the geomatic products obtained. The accuracy of the UAS approach is estimated through direct comparison with on-the-ground household-by-household assessment approaches. Results showed the relevance of surface water flooding and lateral flow flooding, with a total of 168 properties identified as flooded falling outside the fluvial flood extent. The direct tangible losses associated with these additional properties amounted to £3.6 million. The damage-reducing benefits of resistance measures were also calculated. The UAS approach could make a significant contribution to improving the estimation of direct tangible losses.
How to cite: Rivas Casado, M.: Estimating flood direct tangible losses: An Unmanned Aircraft System based approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18043, https://doi.org/10.5194/egusphere-egu25-18043, 2025.
Feature selection is an essential step in the development of empirical flood damage models based on machine learning techniques. So far, most models of this type were developed using data from a single region or country, and few of them utilize harmonized transboundary datasets. Here, we have harmonized 38 variables present in the datasets of three flood damage surveys conducted in Germany (n = 516), the Netherlands (n = 409) and Belgium (n = 320) after the 2021 mega-floods in Europe. After performing data imputation and multicollinearity check, we used linear and non-linear machine learning algorithms to assess permutation importance and identify features most influencing flood damage. The results of the four models suggest that besides the hazard variables such as water depth and human stability, the location of the heating system (in the basement or at a higher floor) appears among the topmost important features for both building and contents damage.
Subsequently, we did an analysis for a low and high range of water depths using the median value (0.6 m) as splitting criteria. In the lower range, for both types of damage, water depth appears to be the dominating driver, and specifically for the building damage, it exceeds by far the importance of any other variable. In contrast, for water depths above 0.6 m other factors outweigh water depth. In the case of content damage, building footprint area becomes the most important factor across all the models. For the building damage some hazard (e.g. human stability), exposure (e.g. building size) and vulnerability (e.g. hazard knowledge) variables have a comparable importance with that of water depth. Hence, our results show that multivariable models appear particularly necessary for modelling flood damage induced by high and extreme hazard conditions.
How to cite: Avila, M. P., Rodriguez Castro, D., Endendijk, T., Lisa, D., Ravikumar, G., Erpicum, S., Thieken, A., Aerts, J., Heidi, K., and Benjamin, D.: Flood damage in the residential sector: on the value of transnational datasets for robust feature selection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18240, https://doi.org/10.5194/egusphere-egu25-18240, 2025.
Quantitative flood risk assessment is essential for local disaster risk reduction and management strategies. However, data scarcity which typically characterizes the Global South, poses significant challenges to the application of conventional risk assessment methodologies developed in data-rich contexts. This study addresses these challenges by providing an exportable and comprehensive flood risk framework designed for the Metuge district, a flood-prone region in northern Mozambique that is crossed by the Muaguide River. This framework integrates hydrological, hydrodynamic, and damage modelling with a multi-level participatory process that involves stakeholders from governmental to community levels. To overcome data deficiency, the modelling leverages global data sources, field survey data, and open-access tools. Feedback gathered through participatory activities has allowed to refine modelling assumptions, enhancing the reliability of the outcome. Specifically, the participatory activities were designed to reach multiple objectives: increasing the building capacity of local authorities, empowering the resilience of the local population, and validating the results. In fact, the absence of observed data for the study area has made the comparison of the results with community experience of past flood events the sole viable option for their validation. Results from this case study indicate an average of 2,000 individuals at risk annually and an Annual Average Damage (AAD) of approximately 300,000 USD/year to roads and buildings. The ratio between the AAD and the population of the study area corresponds to 0.5% of Mozambique’s GDP per capita. Moreover, the district population's access to the hospital during flooded periods has been assessed by analyzing the practicability of roads. These findings provide critical insights for local authorities for flood risk management and serve as a foundation for the design and implementation of mitigation measures.
How to cite: Molinari, D., Rrokaj, S., Paz Idarraga1, C. D., Rotaru, A. M., Ergün, Z., Anza, A., Porzio, M., Costa, A., and Radice, A.: Integrating modelling and community engagement for flood risk management in data scarce contexts: insights from Metuge district in northern Mozambique., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10866, https://doi.org/10.5194/egusphere-egu25-10866, 2025.
The Indian Himalayan region, distinguished by its ecological sensitivity and dynamic topography, suffers significant losses annually due to frequent natural disasters like landslides, earthquakes, and floods. Estimating loss and damage (L&D) is one of the most important tools for disaster risk management. It provides information on post-disaster recovery, resource allocation, redevelopment/rehabilitation project prioritization, and compensations to the affected communities. Calculating the impact of a disaster and developing long-term recovery plans for the Himalayan community specific to the region's unique urban and rural contexts require evaluating and prioritizing the indicators of economic loss and damage (ELD) and non-economic loss and damage (NELD). This study focuses on Udham Singh Nagar and Nainital districts of Uttarakhand, with a structured approach to identify, prioritize, and validate relevant indicators for multi-hazard loss and damage (MH L&D) calculation. Comprehensive datasets from the Uttarakhand Disaster Risk Atlas and government reports are used, including socioeconomic, environmental, infrastructure, and hazard-specific information for earthquakes, landslides, and floods. ELD and NELD indicators are processed and prioritized using a mixed statistical approach that includes principal component analysis (PCA) and the covariance matrix. This method's successful reduction of data dimensionality while maintaining important information made identifying high-priority indicators possible. To direct focused actions in the rural and urban settlements of the Himalayan region, these indicators were then rated. The HAZUS model—a standardized instrument for hazard loss estimation— guarantees the prioritized indicators' validation. Due to varying socioeconomic dynamics, exposure levels, and vulnerabilities, the study found notable differences in priority indicators across rural and urban locations. The findings underscore the importance of region-specific, hazard-sensitive prioritization frameworks for effective loss and damage assessment and disaster risk reduction (DRR). By highlighting the interplay between ELD and NELD indicators across multiple hazards, this study provides a valuable tool for policymakers, planners, and disaster management agencies to target investments in the required sector of the community for their rapid post-disaster long-term recovery. The validated indicators can serve as a baseline for future MH L&D assessments in similar geographies and Gram Panchayat Development Plans (GPDP).
How to cite: Goyal, S. and Mukherjee, M.: ELD and NELD Prioritization for Multi-Hazard Loss and Damage in Rural and Urban Areas of Uttarakhand’s Himalayan Districts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6259, https://doi.org/10.5194/egusphere-egu25-6259, 2025.
With our work, we’re proposing an approach to damage modeling oriented to the potential damage evaluation, with the aim to use it to address the prevention strategies in a more efficient way.
The methodology will be presented on a case study developed in the Italian region of Tuscany. For this application, our data collection on perceived damage, made up of claims compiled by citizens in the aftermath of relevant flood events, has been enriched with new data to cover the whole set of national state of emergency for Tuscany in the period 2013/2023.
Claims have been geolocalized and extracted on the plane areas of the region. We came up with more than 10800 points, providing a picture of where damage occurred, the declared economic losses and the areas affected by more than one event.
This dataset has been later adopted to train a machine learning model which combines the occurred damages, the characteristics of the territory (obtained from digital terrain model and other open data) and the communities’ social variables primarly derived from national census. Instead of superpose hazard and exposure, we have been working combining data sources which are different for origine, scale and semantic area in a big database to be provided to the algorithms.
We are here presenting the results of our work as well as the lesson learned in the modeling procedure.
How to cite: Zambrini, F., Nava, E. M., and Mendui, G.: Damage susceptibility in Italy: a case study for Tuscany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19909, https://doi.org/10.5194/egusphere-egu25-19909, 2025.
The desiccation of Utah's Great Salt Lake (GSL) poses significant health risks, particularly for vulnerable populations. This study examines how the diminishing GSL, exacerbated by anthropogenic changes, affects community mental health. Reduced water inflow has exposed the lakebed, increasing airborne particulate matter and dust storms, which impact air quality. By integrating diverse datasets spanning from 1980 to present—including in-situ measurements, satellite imagery, and reanalysis products—this study synthesizes hydrological, atmospheric, and epidemiological variables to comprehensively track the extent of the GSL’s surface water, local air quality fluctuations, and their effects on community mental health. The findings indicate a clear relationship between higher pollution days and more severe depressive symptoms. Specifically, individuals exposed to ~ 22 days with PM2.5 levels above the World Health Organization's 24-hour guideline of 15 μg/m³ were more likely to experience severe depressive symptoms. Our results also suggest that people experiencing more severe depression not only face a higher number of high-pollution days but also encounter such days more frequently. The study highlights the interconnectedness of poor air quality, environmental degradation and mental health emphasizing the need for more sustainable economic growth in the region.
How to cite: Neelam, M., Bhui, K., and Freitag, B.: Diminishing Waters: The Great Salt Lake's Desiccation and Its Mental Health Consequences, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20664, https://doi.org/10.5194/egusphere-egu25-20664, 2025.
The continuous expansion of impervious areas in megacities, combined with the increasing frequency and magnitude of extreme climatic events, has led to more frequent flood events in urban areas. Flooding, currently the most common disaster worldwide, is an adverse event that can result in significant human impacts (e.g., loss of life, injuries, and illnesses), material damage (e.g., destruction of private and public property), and environmental degradation. These damages also lead to economic and social consequences, such as psychological trauma and social disruption. The watershed of the Aricanduva River, located in the East Zone of São Paulo, Brazil, faces recurrent flooding issues, particularly along its main course, which is adjacent to a critical avenue. These flood events are primarily attributed to the watershed's physical characteristics, including its steep river gradient and extensive urbanization in the lower and middle sections of the basin. This study aims to assess socio-environmental impacts using hydrological modeling and demographic data provided by the Brazilian Institute of Geography and Statistics (IBGE). The methodology is divided into three main steps: (i) Generation of inundation maps for six events using HydroPol2D, a fully distributed and coupled hydrologic-hydraulic model that solves the shallow water equations (SWE); (ii) Spatial analysis of census data provided by IBGE to develop population and household density maps; (iii) Assessment of impact factors, termed “affected population” and “affected households”, through the overlay of flood maps with population and household density maps. It is important to note that the six analyzed events were selected based on alerts issued by the Flood Warning System of the State of São Paulo (SAISP). The study's results reveal how the impacts of recent rainfall events evolve over time and highlight areas with recurrent flooding. These analyses demonstrate that residents in the Aricanduva watershed face considerable flood risks. The methodology implemented for impact assessment can support the development of emergency plans and actions to mitigate the social and economic impacts of flooding. These measures are closely aligned with the Sustainable Development Goals (SDGs), specifically Goals 6.5, 9.1, 11.5, and 13.3, as well as the United Nations' Sendai Framework for Disaster Risk Reduction (SFDRR). The data generated in this study could serve as a reference for future analyses, such as evaluating the effectiveness of urban drainage plans, future flood warning systems, or other flood control strategies. Additionally, the watershed model could be utilized to develop an assessment framework for indirect impacts, including the potential effects of flood events on accessibility to critical areas, disruptions to economic activities, and transportation. This would enable proactive planning and the identification of alternative solutions in advance.
Keywords: Natural hazards, Urban flooding risk management, Socio-environmental impacts, Hydrological-hydrodynamic models, Climate change.
How to cite: Hernandez Sanchez, M., Silva, P. G., Silva, G., and Mendiondo, E. M.: Assessing flood impacts in an Urban watershed in São Paulo City, Brazil, using a fully distributed and coupled Hydrological-Hydraulic model and demographic statistics., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12542, https://doi.org/10.5194/egusphere-egu25-12542, 2025.
Climate change poses a major challenge to the insurance industry, highlighting the need for sustainable crop insurance programs to protect food production in developing countries amid increasing climate risks. Insurance plays a key role in advancing SDGs 1 (no poverty), 2 (zero hunger), and 13 (climate action). However, the short- and long-term impacts of climate-driven extreme weather events remain insufficiently understood. This study maps major climate threats to crop production in Brazil and examines the influence of extreme weather on price adjustments and insurance uptake. The research used a database of the Brazilian Program of Subsidies for Rural Insurance Premium (PSA) with 1.5 million policies and claims from 2006 to 2023 and meteorological daily data from the Brazilian Daily Weather Gridded Data (BR-DWGD) from 1991 to 2024, both aggregated at municipality level. Four perennials and non-perennials crops were observed as the most insured: soybeans (47%), maize second cycle (13.5%), wheat (8.7%), and grapes (8.9%). Moreover, the most critical hazards were droughts (43.1%), hail (34.4%), frost (10.3%), excessive rainfall (8.2%), floods (1.3%), cold winds (1.0%), and temperature variation (0.3%). In 2021, claim payments reached a historic high, totaling nearly 12 billion BRL, which was unprecedented when compared with the baseline annual values ranging from 0.043 to 2 billion BRL. Two critical periods significantly impacted crop production in 2021. The first occurred between March and April (Austral fall), with severe droughts and frost events affecting maize second cycle in southern and central-western states and wheat in the south. The second critical moment was between August and October (end of Austral spring and beginning of Austral summer), in which major droughts affected soybean production in the states of south, southeast, central west and northeast. The impact on only three crops explain the expressive increase in claim payments. From 2019 to 2023, soybean prices revealed significant evidence of weather shock impacts, as a major driver of premium rates increase. Rates increased by 38%, growing from 344.33 BRL/ha in 2021 to 562.12 BRL/ha in 2022. The insurance uptake increased 11% in 2022, growing from 188,179 to 212,839 policies and had a dramatic decrease of 72% in the following year. The analysis of insurance and weather data highlights significant impacts of increased climate-related stress on Brazil's insurance industry. Initially, unprecedented extreme events tend to drive an increase in insurance uptake. In response, insurance companies often raise premium rates to maintain a balanced ratio between revenue and payouts. However, this adjustment can lead to a subsequent decline in insurance uptake. Climate shocks may have prolonged effects on insurance, potentially undermining the financial sustainability of farmers and their capacity to recover from economic losses. Thus, these dynamics underscore the need for adaptive strategies to ensure resilience in both the insurance sector and agricultural systems.
How to cite: Benso, M. R., Silva, G. M. E., Silva, P. G. G. D., and Mendiondo, E. M.: Multidimensional indicators of sustainability of crop insurance under increased climate-stress, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13435, https://doi.org/10.5194/egusphere-egu25-13435, 2025.
Integrated flood risk management requires an extension from hazard to risk analysis and an involvement of various stakeholders including the general public. Since no standard protocols for collecting data about flood-affected societies are in place, post-disaster surveys have been initiated to gain information from affected residents and companies. Using the most damaging flood events that have occurred in Germany since 2000 as examples, the lecture will address how data collected from flood-affected people have been used a) to develop and improve loss models, b) to better understand how and why people adapt to flood risk, c) to evaluate how people respond to warnings, d) to provide insights into flood-related health impacts and e) to comprehend how people recover from flood impacts. Since flood processes in Germany between 2002 to 2024 differed considerably, it will be addressed how much the flood type – in particular slow-onset river flooding, flash floods and pluvial floods – influence impacts and coping mechanisms. Research outcomes have informed flood early warning systems, risk communication and recovery programs in Germany and beyond. However, surveying or interviewing flood-affected people might also put an additional burden on them. Hence, the lecture will discuss some ethical considerations about collecting data in (highly) affected areas as well as some pros and cons of cross-sectional versus longitudinal survey designs. Finally, transfer to other regions and hazards will be highlighted.
How to cite: Thieken, A.: More than two decades of post-disaster household surveys to improve flood risk management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6429, https://doi.org/10.5194/egusphere-egu25-6429, 2025.
Reliable road infrastructure is vital for daily commuters and economic activities in the UK, yet it faces growing flood risks due to climate change. Effective flood risk management requires an integrated approach that includes pre-disaster traffic flow modelling, direct damage estimation, disruption and recovery analysis to quantify systemic failure impacts. Indirect costs from traffic disruptions are frequently oversimplified, often estimated as multipliers of direct damages. While traffic flow rerouting models are applied in current research, they often overlook critical factors, such as traffic flow constraints and road capacity limitations, instead assigning origin-destination flows to least-cost paths without accounting for congestion. Moreover, the recovery process, which is critical for understanding how restored road and bridge capacities reduced isolated flows and indirect damages, is rarely modelled.
To address these gaps, we developed an open-source modelling framework for Great Britain that integrates a process-based flow model with a stress-testing model to assess road flood damages. Our framework starts with simulating passenger-to-work flows at a national scale by modelling the lifeline connections between physical road networks and demographic factors (e.g., population and economic activities). The flow model employs an iterative approach to simulated congested equilibrium flow assignments, dynamically accounting for road capacities and flow speeds until all traffic is accommodated without causing overflow.
We stress-tested the road networks using 18 historical UK flood events and one synthetic flood event. To model flood-induced disruptions, we developed a speed-flood depth function that restricts maximum flow speeds on flooded roads based on floodwater depth. We applied 30cm and 60cm separately in disruption analysis as threshold for road closure for uncertainty analysis. In each scenario, flood impacts on traffic flows were evaluated by comparing edge flows under floods with those under base flow condition. Direct damages were calculated using generalised damage curves (i.e., function to estimate damage fractions based on floodwater depth), and cost functions (i.e., function to estimate unite asset cost, million £/length or area) for different road types (e.g., bridges, tunnels, ordinary roads) and flood types (e.g., surface floods, river floods). Indirect damages were quantified by calculating rerouting costs due to road closures, including additional fuel costs, tolls, and time-equivalent costs.
We introduced a novel recovery analysis to dynamically evaluate indirect damages by designing various road capacity recovery rates, accounting for road types and damage levels. The recovery process identifies disrupted flows resulting from missing routes or reduced speeds, and reallocates these flows as road capacities are restored on a daily basis. The analysis captures the evolving number of isolated flows, rerouting costs and asset repair costs, offering a more realistic representation of dynamic indirect damages.
Overall, this research advances large-scale flow modelling by integrating capacity constraints, disruption dynamics, and recover processes. It provides actionable insights to enhance the resilience of the UK’s road infrastructure. The framework can be adapted to contexts beyond UK, different spatial scales, multi-modal transport systems, and multi-hazard scenarios, supporting more comprehensive risk assessments and decision-making.
How to cite: Li, Y., Pant, R., Russell, T., Thomas, F., Hall, J., and Oldham, P.: A Process-based Flow Model for Assessing Direct and Indirect Damages to Flooded Roads in Great Britain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9365, https://doi.org/10.5194/egusphere-egu25-9365, 2025.
The unexpected and destructive nature of natural catastrophes may cause major shocks to communities and jurisdictions at all levels of governance. The goal of this study is to reorient national and subnational research to focus on the connection between natural disasters and economic growth. This research experimentally investigates the economic impacts of floods and cyclones at the subnational government level in India. For this, the study created a balanced panel of 24 Indian states from 1995 to 2018, using GDP, sectoral growth data, and the Disaster Intensity Index created from the impact of cyclones (using windspeed data) and floods (gridded precipitation data). The study finds that disaster shock negatively affects overall economic and sectoral growth in the Indian states. Dissecting the economic growth in terms of sectoral growth, the study observed that output growth of agriculture, industry, and service sectors all have a negative impact in the initial year of disasters, which negatively affects state GDP per capita. The results align with the predictions from endogenous growth theory. The model also shows a positive effect for the service sector in the second year and for the agricultural sector in the third year after the disasters. This study may encourage decision-makers to focus on developing India's overall resilience by implementing efficient and long-lasting preventive measures before the disaster occurs and ensuring quick response, recovery, and reconstruction during and after the disaster.
How to cite: Suresh, N. and Mishra, T.: Storms and Surges: Evaluating the Effects of Floods and Cyclones on Sectoral Growth at Sub-National Level in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7997, https://doi.org/10.5194/egusphere-egu25-7997, 2025.
The increasing frequency and intensity of tropical cyclones, driven by climate change, pose significant risks to global supply chains, amplifying economic vulnerabilities. This study explores how interactions between multiple extreme events influence the propagation of indirect economic costs, focusing on the compounding effects that arise within interconnected systems. Leveraging a combination of the CLIMADA risk modeling platform and the ARIO indirect impact economic model, we generate synthetic ensembles of tropical cyclones. These simulations allow us to analyze direct and indirect economic impacts at global and regional scales.
Our results reveal that compounding events can, in some cases, mitigate indirect losses. This effect arises from the accumulation of reconstruction demand, which stimulates production across sectors, particularly those heavily involved in rebuilding, such as construction and manufacturing. The interplay between reconstruction demand and overproduction mechanisms creates a virtuous cycle, accelerating recovery and offsetting consequent losses.
However, the observed mitigation is highly dependent on the underlying modeling assumptions and sectoral resolution of the modeled economy. Indeed, some adverse indirect economic consequences only emerge when employing economic data with a higher granularity of sectors. When such higher granularity of sectors is combined with less optimistic assumptions on adaptation capacity, not only does the mitigation effect disappear, but observed outcomes show significantly aggravated indirect losses.
This study underscores the complexity of modeling compounding risks and highlights the importance of carefully chosen parameters and granularity of economic data, as qualitatively different results can emerge. In this context, ARIO serves as an effective tool for exploring the drivers of indirect economic impacts from extreme events, providing valuable insights to guide and enhance more advanced modeling approaches.
How to cite: Juhel, S., Stalhandske, Z., Viguié, V., and Bresch, D. N.: Understanding the interactions of tropical cyclones in global supply chains, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8509, https://doi.org/10.5194/egusphere-egu25-8509, 2025.
With globally interconnected economies through supply chains, the economic impacts of flooding—one of the most devastating natural disasters—pose significant concerns for both direct flood-affected countries and their trade partners. This underscores the need for a global assessment of these direct economic impacts and their potential propagation to develop flood-resilient supply chains on a global scale. Here, to assess the generic global flood risks, we evaluate direct economic losses across different sectors and propose indicators for assessing the indirect impacts of flood propagation through international trade. We demonstrate that the estimated global annual economic loss across agricultural, industrial, and service sectors is US$194 billion. China, India, the USA, Indonesia, and Egypt are significant sources of flood-related risks due to their considerable direct economic losses and diverse export partners, collectively accounting for more than 50% of the global direct economic loss. Meanwhile, emerging and developing countries in Asia and Africa and some developed countries with concentrated imports from high-risk-giving countries show significant potential to be affected by flood impacts indirectly; the relevance of indirect risk to these countries differs from the sector. These findings highlight the importance of a sector-wise assessment of flood economic impacts and their potential propagation via trade. Therefore, the assessment methods and indicators developed in this work will help inform policy and investment decisions for building flood-resilient supply chains and supporting business continuity plans.
How to cite: Mtibaa, S., Maeno, K., Islam, K., and Motoshita, M.: Assessing the global economic impacts of floods and their potential propagation through international trade, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1874, https://doi.org/10.5194/egusphere-egu25-1874, 2025.
Natural hazards such as floods pose significant threats to communities worldwide, impacting lives, livelihoods, and infrastructure. Effective flood modelling in combination with accurate impact assessments are crucial for enabling timely interventions, effective emergency management, reducing disaster-related losses and enhancing societal resilience.
This work presents recent advancements in a comprehensive toolset for worldwide application, integrating advanced flood and impact modelling in a flexible way. Our tools quantify critical aspects of emergency planning and management, including the estimation of the number and location of (socially vulnerable) people affected by floods, the identification of individuals that should be warned and evacuated, the impact on populations in terms of accessibility, the identification of potential and available evacuation and provisioning routes before, during and after hazards, and the resultant time and distance for populations to the nearest shelter. We integrate social vulnerability and socio-economic characteristics into our analyses to prioritize socially vulnerable areas.
Our dynamic modeling of flood depths, extents, and durations utilizes the open-source flood model 'SFINCS'. The open-source impact model ‘RA2CE’ quantifies disruptions to road infrastructure due to any natural hazard, equipping road operators, spatial planners, and emergency managers with actionable information. The advancements were recently applied to a real-world case study in Mozambique in a participatory workshop with local stakeholders from the cities of Beira and Quelimane.
This work provides applicable solutions for prevention, planning, early warning, response and recovery, extending across most disaster risk phases and significantly contributing to the Sendai Framework's goal of reducing disaster risk and losses in lives and livelihoods.
How to cite: Meijer, L., de Goede, R., Costa de Barros, E., Gunaratne, C., Hauth, M., van Marle, M., Nobre, G., and van Dongeren, A.: Comprehensive Flood and Impact Modeling: Advanced Quantitative Tools for Emergency Management and Societal Resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17951, https://doi.org/10.5194/egusphere-egu25-17951, 2025.
Posters on site: Wed, 30 Apr, 08:30–10:15 | Hall X3
In light of the increasing losses from flash floods, exacerbated by climate change, there is a pressing need for robust flash flood loss models to support risk analyses and mitigation strategies. Existing residential sector loss models predominantly focus on fluvial flood processes; while the key drivers of flash flood losses remain poorly understood. Applying Machine Learning on empirical data reveals key drivers of flash flood losses such as flow velocity and emergency response. We introduce FLEMOflash (Flood Loss Estimation MOdel for flash floods), a novel multivariate probabilistic model to estimate losses to residential buildings and contents from flash floods. Model based assessments reveal that households with clear knowledge of emergency action during high water levels can reduce building losses by up to 78% and contents losses by up to 31%. Thus, FLEMOflash can provide differential loss estimates based on varying levels of risk preparedness.
How to cite: Guntu, R., Sairam, N., and Kreibich, H.: FLEMOflash: The probabilistic flash flood loss model for quantifying direct economic losses with uncertainty information, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-649, https://doi.org/10.5194/egusphere-egu25-649, 2025.
Climate change has now become a major issue for the whole world, as it not only brings unprecedented extreme weather events but also causes major disruptions to natural habitats and ecosystems. The impact of climate change on nature reduces the resources that nature provides, known as nature-related dependencies, while also increasing the disturbances caused by nature, known as nature-related impacts. These issues have also received attention from the Taskforce on Nature-related Financial Disclosures (TNFD). This research is crucial because under climate change circumstances, corporations will face numerous operational hazards; while affecting nature, climate change also has a great impact on businesses, and companies that rely on natural resources have been negatively affected to a significant extent. Based on these concerns, we would like to investigate how climate change will affect nature-related issues.
Therefore, in this study, we will discuss climate change and nature-related issues, especially the two factors of dependence and impact, and analyze the changes in nature-related dependencies and impacts that climate change will bring to specific industries. The analysis will be divided into four major steps:
1. We will use three different types of Representative Concentration Pathway (RCP) scenarios to simulate possible changes in temperature and rainfall under different future scenarios.
2. We will establish how nature-related dependencies and impacts will change as temperature and rainfall change under climate change.
3. We will use qualitative methods to grade the degree of change in nature-related dependencies and impacts from very low to very high, and use a visual method such as a Heatmap to present the results.
4. We will link these analyses to assess how climate change will affect the severity of nature-related dependencies and impacts across different industries, enabling them to quickly understand the specific challenges they will face. This will include integration of ENCORE for more detailed, sector-specific analysis.
The final outcome we expect to achieve is presenting the visualization results using a Heatmap to show the combination of climate change simulation on nature-related dependencies and impacts, and the industry-based data according to ENCORE, demonstrating how climate change will affect industrial nature-related issues.
This comprehensive framework will enable corporations to better understand the nature-related risks they may face under future climate change and adapt to evolving nature-related challenges while considering broader factors to mitigate risks and seize opportunities for sustainable growth.
How to cite: Chung, T.-J. and Tung, C.-P.: Assessing Climate Change Implications on Nature-Related Dependencies and Impacts: A Scenario-Based Approach for Industry-specific Insights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2337, https://doi.org/10.5194/egusphere-egu25-2337, 2025.
The adequate implementation of flood risk reduction measures depends on our ability to quantify flood losses robustly and accurately. Existing flood loss models have been constructed using data or experience from past flood events. Frequently, in the existing datasets, extreme damage mechanisms, such as severe structural damage to buildings, are underrepresented, and the corresponding losses are often overlooked. New datasets collected after the European flood of 2021 provide an opportunity to improve existing tools for predicting the degree of flood-induced structural damage to buildings. In this study, a classification model for severe structural damages to residential buildings is developed using data on building damage during the 2021 flood in Belgium. A new damage grade typology was created on the basis of 197 engineering reports investigating the stability of individual buildings. Moreover, building and hazard characteristics were extracted from these reports and complemented with additional data, obtained from hydrodynamic simulations, field surveys, and cadastral data. A logistic classifier using hazard and building features was built to predict whether or not buildings suffered severe structural damage. This final model can be used for a preliminary post-event assessment of structural damage to support the allocation of resources and to prioritise interventions to buildings. It can also be included into existing flood loss models to improve the representation of extreme damage mechanisms.
How to cite: Rodriguez Castro, D., Paterka, A., Cools, M., Archambeau, P., Erpicum, S., Pirotton, M., and Dewals, B.: Structural damage grade classifier for residential buildings based on the July 2021 flood event in Belgium, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2845, https://doi.org/10.5194/egusphere-egu25-2845, 2025.
Wind and solar power supply and demand mismatches, intensified by climate change, can potentially lead to power shortages that profoundly disrupt highly interconnected global supply chains. Here, we assess the domestic and international economic impacts of climate-driven power supply/demand mismatch risks on global supply chains and highlight the vulnerabilities within each country-sector supply chain. We find that, domestic economic losses, ranging from 0.1% to 18.2% of total output, are generally positively correlated with national power shortage risks. Meanwhile, international indirect losses vary significantly across supply chains, exhibiting a “trade trailing effect” that takes 1–11 months to propagate and an additional 1–9 months to recover, as well as a “butterfly effect” that amplifies international losses in high-risk small economies, sometimes by factors of ten or more. Small economies are particularly sensitive to disruptions, especially upstream impacts on agriculture-oriented economies and downstream disruptions in energy-related sectors in high-risk economies. Our findings provide valuable insights into trade resilience under climate change.
How to cite: Yaqin, G., Liying, P., and Dan, T.: Amplified trailing economic losses in global trade by climate-driven wind and solar supply-demand mismatch, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3666, https://doi.org/10.5194/egusphere-egu25-3666, 2025.
Flood events can significantly disrupt economic activities, yet the relationship between flood characteristics and business downtime remains underexplored. Downtime estimates are currently based on expert evaluations, the differentiation by sector type is highly aggregated, and assessments based on observed data are very limited. This study leverages a comprehensive database of post-flood information collected in Germany to examine how flood hazard characteristics and exposure attributes of economic activities influence the duration of operational interruptions. The research objectives are: (1) to investigate the correlation between various flood hazard characteristics and resulting business downtime, and (2) to assess the relationship between direct damages and downtime, accounting for the specific attributes of exposed entities.
The database includes detailed information collected via telephone interviews conducted after flood events in the period of 2002 - 2013. Variables encompass hazard characteristics (e.g., water depth, event duration), exposure characteristics (e.g., industrial sector, number and type of buildings, equipment and stock values), impact measures (e.g., total damages to buildings, equipment, and goods, downtime duration), and adaptation strategies (e.g., emergency plans, alarm times, protective measures). Key variables are classified into independent (e.g., hazard characteristics), dependent (e.g., downtime measures), and control categories (e.g., qualitative and descriptive responses). The analysis is adopting traditional statistical methods, including Pearson's correlation, regression analysis, and ANOVA, to evaluate linear relationships, alongside machine learning techniques—such as clustering, decision trees, random forests, and neural networks—to uncover complex, non-linear interactions among variables.
The findings of this research will provide valuable insights into the dynamics of business interruption and contingent business interruption caused by flood events. By expanding the understanding of how hazard characteristics, exposure attributes, and adaptive strategies interact to influence downtime, this study lays the groundwork for advancing risk assessment models of natural hazard into economic sectors. These results will not only support the insurance sector in evaluating and managing collective risks but also contribute to the development of more robust strategies for enhancing societal and economic resilience to natural hazards.
How to cite: Arosio, M., Nobile, E., Bautz, P., Cesarini, L., and Sairam, N.: Flood impact on business downtime: analysis of post-flood observed data in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6961, https://doi.org/10.5194/egusphere-egu25-6961, 2025.
Estimating flood damage is crucial for both disaster risk reduction in the prevention phase and crisis management during flood events. While models for predicting damage from riverine floods are well-developed, tools for estimating damage from urban pluvial flooding are less advanced. This is an important gap, as heavy rainfall can lead to flooding in a wide range of locations, not just along rivers.
Here, we present a new machine learning-based tool to quickly estimate building-level damage from urban pluvial flooding caused by heavy rainfall. Three key improvements are incorporated into this tool, compared to the traditional use of stage-damage models developed for riverine floods in dismissal of the flood pathway or the use of newer, overly complex models. First, it was trained on data specifically from known urban pluvial flood events, rather than relying on models developed for riverine floods, which can lead to more accurate damage estimates for this type of flooding. Second, the tool utilizes the XGBoost algorithm, a powerful machine learning technique capable of capturing complex non-linear relationships in the data. Third, the tool's modular design allows users to efficiently utilize available geographical information when making damage estimates by fixing the area of interest and reducing one step of the data preprocessing, towards providing results quickly enough for real-time forecasting applications. To address the common challenge of missing data, the tool uses smart random sampling techniques to impute required building-level features that are representative to known buildings affected by this flood pathway, reducing exposure bias.
The performance of the new tool was evaluated in two case studies in Germany, involving approximately 2,400 and 17,500 buildings, respectively. The tool was able to provide damage estimates in 1.1 and 6.0 minutes on a standard laptop, representing a 2-3 fold improvement in speed compared to a baseline approach. Furthermore, to validate the tool, estimates were compared to a fully independent dataset. The new tool reduced the estimate error by a factor of 4.3 compared to employing a riverine flood damage model, demonstrating its improved accuracy for heavy rainfall flooding events, although generally showing overestimation.
The new tool, named FlooDEsT – Flood Damage Estimation Tool, comprising the damage function and its application strategy, has shown improvements in computation time and performance at the first pilot studies. Its expansion to other flooding events and comparison with other damage datasets shall clarify its generalization power towards an improved estimation of building damage at urban pluvial floods.
How to cite: Samprogna Mohor, G., Lindenlaub, S., and Thieken, A.: Fast and operational building damage estimation tool for urban pluvial flooding, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10095, https://doi.org/10.5194/egusphere-egu25-10095, 2025.
Natural hazards pose significant risks to cultural heritage, leading to monetary losses and fatalities annually. Hazard exposure encompasses spatial, quantitative, and qualitative aspects of potentially impacted elements. Cultural heritage necessitates the integration of both intangible and tangible values in risk assessment frameworks for various reasons, including prioritization in the safeguarding of cultural heritage assets and effective risk management. This study introduces a participatory, quantitative approach to evaluate the social value of cultural heritage for the assessment of natural hazard exposure. The research specifically addresses the challenge of incorporating intangible values, particularly social value, into risk assessment. The methodology employs a web-based pairwise comparison survey where participants answer the question "Which among the following cultural heritage items would you recommend to a friend?" for pairs of heritage items. Each item is presented with a photo, name, and brief description, with pairs selected using the Swiss tournament method to maximize item occurrence. The survey platform, developed using open-source tools (Python, Flask, and MariaDB), transforms qualitative preferences into quantitative scores through eigenvalue analysis of the resulting pairwise comparison matrix. The method was applied to Florence historical city center, a UNESCO World Heritage site, where 48 heritage buildings were evaluated through 2379 survey responses from the community of local cultural association members. When combined with flood hazard data, the methodology demonstrated how incorporating social values can substantially alter the spatial distribution of exposure compared to traditional hazard mapping. The methodology provides a replicable tool for assessing intangible values in cultural heritage exposure analysis, though results may vary depending on the participating community. This research contributes to improved risk management and prioritization of mitigation measures by incorporating community-based valuation into intangible exposure assessments.
Acknowledgement:
This work was carried out within RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005)
How to cite: Masi, M., Arrighi, C., and Castelli, F.: A participatory pairwise comparison method for assessing social value of cultural heritage in risk analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10799, https://doi.org/10.5194/egusphere-egu25-10799, 2025.
During the past two decades Denmark has experienced a dramatic increase in annual damages from flooding. Multiple cloudbursts, one of which was registered as the most costly natural hazard event in Northern Europe that year, several sea surges and the wettest winter season ever recorded, leading to excessive floodings in lowlying areas and fluvial flooding. This increase in occurences of floods is expected to further accelerate in the coming decades, leading also to a drastic increase in compound events, i.e. several phenomena occurring simultaneously exacerbating the flood event. Measures to keep societal risks at acceptable levels are highly needed. When designing strategies and concrete measures theoretically calculated damages given flood events are critical.
The dominating existing paradigms for assessing cost given events is to generate damage-depth curves for each type of flooding and cost category. The curves are assessed based on either a bottom-up approach based on e.g. asset characteristics (e.g. buildings in UK) or top-down approaches based on insurance claims, surveys or other aggregate data (e.g. Germany and Denmark). Both bottom-up and top-down approaches have shortcomings, the first method is based on assumptions that are difficult to verify and the second is based on data that are often biased and difficult to achieve because of restrictions in GDPR-regulation, privileged information held by private companies, and that the value of many assets cannot be assessed on a free economic market. Further, both approaches fail to capture essential characteristics of the damage costs, notably that the damage is dependent on the source of the flooding which in many cases in the future will be compound events and hence a function of two or more distinctly different damage-depth curves. The interplay between different cost categories are also often ignored.
The current project aims at generating knowledge that enable unifying damage-depth curves across water sources and damage categories. This will be done by combining desktop studies with novel uses of data collected at both governmental agencies and private entities such as insurance companies. However, important extensions to the traditional frameworks will be to include an assessment of how the damage-depth relationships is expected to change over time. Many analyses ignore the learnings and adaptations that will occur in the future and that recovery periods may be extensive and lead to societies that are either more or less resilient based on the strategy for recovery. Most notable is the assumption that an asset will suffer the same economic damage now and in the future even though the flood frequency will in some cases change from 1 in 50 years to every year. In these cases the damage from each event will undoubtedly decrease. A more explicit incorporation of the disaster management cycle into the assessments will also allow for a more realistic assessment of damages as they become more and more severe in a given region and larger events with longer recovery periods will be more prevalent.
How to cite: Arnbjerg-Nielsen, K., Panduro, T. E., and Brandt, U. S.: Revisiting methodologies for damages caused by flooding across water sources, damage categories, and spatio-temporal scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12650, https://doi.org/10.5194/egusphere-egu25-12650, 2025.
Here we present a systematic approach to developing an urban poor-centred (multi-)hazard impact classification using multiple data source types, with application to the Kathmandu Valley, Nepal. Marginalised communities, including urban poor communities, are typically neglected from impact data sources, despite these groups often experiencing disproportionate impacts of (multi-)hazard events and having a lower capacity to respond. Gaps in impact data are particularly challenging in regions of data scarcity, where comprehensive evidence bases would support the refinement of existing DRR strategies.
We extracted (multi-)hazard impact exemplars from disaster databases (DesInventar Sendai and the Nepal DRR Portal) and newspaper articles (LexisNexis online newspaper archive) utilising systematic (Boolean) searches. We applied the searches to earthquake, flood, landslide and urban fire events owing to their prevalence in the study area. Following this, we manually reviewed the results for relevancy to specific named informal settlements in the Kathmandu Valley. We supplemented these data with insights from three focus group discussions (FGDs) conducted with residents of informal settlements in the Kathmandu Valley and 11 semi-structured interviews with DRR practitioner stakeholders working with these communities. We co-facilitated the FGDs with members of Nepal Mahila Ekata Samaj (NMES, https://mahilaekata.org/), a network organisation of landless women in Nepal.
We compiled the disaster database, newspaper article, FGD and semi-structured interview results into an Excel database of urban poor-centred (multi-)hazard impacts across the four natural hazard types. Within each row of the database, we included details of the source type, (multi-)hazard event details, and impact information categorised by type. Our results indicated that the disaster databases (45 relevant exemplars) presented an overview of (multi-)hazard event details. However, documentation of impacts was typically restricted to quantitative tangible impacts – including economic losses and fatalities. Newspaper articles (83 relevant exemplars) provided nuance to descriptions of (multi-)hazard impacts, with quotes from affected individuals adding socio-political context. Finally, FGD and semi-structured interview participant perspectives of (multi-)hazard events offered richness through lived experience and qualitative accounts, with an emphasis on disaggregated and intangible impacts.
Applying an iterative approach, we compiled the results into an urban poor-centred (multi-)hazard impact categorisation. This typology summarises the impacts, grouped into categories and subcategories, that affect members of urban poor communities in the (multi-)hazard context of the Kathmandu Valley. In gathering multiple data sources of (multi-)hazard impact, we illustrate the value of supplementing quantitative and qualitative data to evidence a more holistic understanding of impact in data-scarce regions, with the intention of centring urban poor community perspectives. We suggest that our methodology and the development of the urban poor-centred (multi-)hazard impact categorisation could provide a framework for scalability to other data-scarce regions, supplementing existing evidence bases to support more inclusive DRR strategies.
How to cite: Thompson, H. E., Taylor, F. E., Malamud, B. D., Gill, J. C., Šakić Trogrlić, R., and Duncan, M.: Developing an urban poor-centred (multi-)hazard impact categorisation using multiple data sources: an application to the Kathmandu Valley, Nepal, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17659, https://doi.org/10.5194/egusphere-egu25-17659, 2025.
