landslides and debris flows, which pose a significant threat to modern societies on a global scale. The
continuous increase of population and urban settlements in hazard-prone areas in combination with
evidence of changes in extreme weather events lead to a continuous increase in the risk associated with
weather-induced hazards. To improve resilience and to design more effective mitigation strategies, we need
to better understand the triggers of these hazards and the related aspects of vulnerability, risk, mitigation and
societal response.
This session aims at gathering contributions dealing with various hydro-meteorological hazards that address
the aspects of vulnerability analysis, risk estimation, impact assessment, mitigation policies and
communication strategies. Specifically, we aim to collect contributions from academia, industry (e.g.
insurance) and government agencies (e.g. civil protection) that will help identify the latest developments and
ways forward for increasing the resilience of communities at local, regional and national scales, and
proposals for improving the interaction between different entities and sciences.
Contributions focusing on, but not limited to, novel developments and findings on the following topics are
particularly encouraged:
- Physical and social vulnerability analysis and impact assessment of hydro-meteorological hazards
- Advances in the estimation of socioeconomic risk from hydro-meteorological hazards
- Characteristics of weather and precipitation patterns leading to high-impact events
- Relationship between weather and precipitation patterns and socio-economic impacts
- Socio-hydrological studies of the interplay between hydro-meteorological hazards and societies
- Hazard mitigation procedures
- Strategies for increasing public awareness, preparedness, and self-protective response
- Impact-based forecast, warning systems, and rapid damage assessment.
- Insurance and reinsurance applications
Flash floods are a major natural hazard in Mediterranean regions, causing significant damage to property, infrastructure, and loss of life. Climate change plays a crucial role in altering rainfall patterns, thereby directly affecting flash-flood behavior. The Mediterranean, a recognized climate change hotspot, is expected to experience more intense extreme rainfall events alongside decreasing total rainfall, both of which may influence flash-flood severity, with responses further modulated by land-use characteristics. Despite substantial research efforts, key gaps remain in understanding flash floods across scales, particularly regarding event-based assessments using high spatiotemporal resolution distributed models capable of capturing flash-flood dynamics in heterogeneous catchments and their sensitivity to climate-driven rainfall changes across catchment sizes, land-use types, and local rainfall characteristics.
This study addresses these gaps by investigating flash-flood behavior in the large Mediterranean Yarkon–Ayalon catchment, located in central Israel, covering 1,800 km². The catchment is characterized by pronounced spatial heterogeneity. The upper part is mountainous and dominated by natural and forested areas on highly permeable Terra Rossa soils, resulting in high infiltration rates. In contrast, the lower part of the catchment is flatter and characterized by lower infiltration rates due to heavy Grumusol soils underlying extensive agricultural land and widespread urban development, with built-up areas covering approximately 70% of the area, promoting rapid runoff generation during rainfall events. A unique streamflow network in the catchment includes 14 hydrometric stations spanning a wide range of spatial scales (7–953 km²) and dominant land use, enabling a multi-scale, multi-land-use evaluation of flash-flood response.
We employ the Grid-Based Hydrological Distributed Runoff (GB-HYDRA) model, an event-based, high-resolution (100 m, 5 min) hydrological model, developed to capture runoff and flash-flood dynamics. The model’s input includes high-resolution radar rainfall data, and it computes runoff at each grid cell and streamflow at any channel cell. To calibrate and evaluate model performance, 37 historical flash flood events with varying intensities and durations are simulated. Of these events, 24 were used for calibration and 13 for independent validation, and 5 hydrometric stations are excluded from calibration, allowing a fair evaluation of the model’s ability to simulate streamflow in ungauged locations. Calibration is performed using a multi-objective optimization approach, resulting in moderate overall model performance, with KGE values of approximately 0.75 for runoff volume and 0.70 for peak discharge across stations and spatial scales.
As a next step, we utilize high-resolution rainfall simulations for a set of storms, derived from the Weather Research & Forecasting (WRF) model under historical conditions and end-of-century projections (RCP8.5), as input to the calibrated hydrological model. The analysis focuses on comparative changes in flash-flood properties across different parts of the catchment and as a function of spatial scale and dominant land use. The results will provide insight into the processes linking changing rainfall patterns to flash-flood response, advancing understanding of flash-flood dynamics across scales in Mediterranean catchments and supporting improved flash-flood risk assessment under climate change.
How to cite: Levin, O., Rinat, Y., Armon, M., and Morin, E.: Multi-scale impacts of climate change on flash floods in a heterogeneous, mixed land-use Mediterranean catchment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4494, https://doi.org/10.5194/egusphere-egu26-4494, 2026.
Reliable and spatially consistent information on flood impacts is essential for understanding recent flood risk patterns and supporting risk assessment and management across Europe. However, existing flood impact databases are often fragmented, rely on heterogeneous documentary sources, and provide limited spatial detail, particularly for recent years.
In this contribution, we present a harmonised, event-based European database of flood impacts covering the period 2015–2024. The database provides spatially explicit estimates of flood impacts for flood events detected by the Copernicus Global Flood Monitoring (GFM) system within a pixel-based framework. Flood depth maps derived from SAR satellite observations using a JRC-developed algorithm are combined with harmonised exposure datasets, including population, land use, transport networks and critical infrastructure, to derive indicators of economic and social impacts such as flooded area, affected population, exposed assets and estimated direct economic losses.
Impact indicators are computed for each event and aggregated at NUTS2 administrative level, enabling harmonised regional-scale assessments across Europe. Although individual event-level estimates are subject to uncertainty, the uniform treatment of events allows robust interpretation of relative spatial and temporal patterns of flood impacts.
The results highlight pronounced interannual variability and strong spatial heterogeneity of flood impacts, illustrating that similar numbers of flood events can lead to substantially different impact outcomes depending on their location and affected assets. By providing a systematic, measurement-based perspective on recent flood impacts, this database complements existing documentary-based datasets and offers a valuable resource for flood risk research, model evaluation and European-scale risk assessments.
How to cite: D'Angelo, C., Betterle, A., and Salamon, P.: A harmonised European database of flood impacts derived from satellite observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9456, https://doi.org/10.5194/egusphere-egu26-9456, 2026.
This contribution presents a systematic framework for managing residual flood risk in embanked fluvial systems, focusing on the Enza River (Italy), a right-bank tributary of the Po River. Even with planned structural and maintenance measures, the fluvial system cannot safely convey extreme flood events (e.g., 500-year floods). Under these conditions, controlled overflows implemented through engineered spillways offer a robust risk-mitigation strategy, enabling the controlled release of floodwaters and reducing the consequences associated with accidental levee failure.
The proposed approach integrates two-dimensional hydrodynamic simulations with the PARFLOOD model to delineate levee segments susceptible to overtopping, support the iterative optimization of spillway location and design parameters, and simulate flood inundation resulting from both uncontrolled levee breaches and controlled overflow conditions. Impact analyses are carried out using advanced tools developed under the MOVIDA project to quantify potential damage to population, infrastructure, and economic assets.
The analysis of multiple flood scenarios (ranging from uncontrolled breaches to controlled overflow configurations, with and without complementary mitigation measures) demonstrates the strong potential of controlled overflows through engineered spillways to reduce flood impacts. The results indicate that controlled overflows can reduce inundated areas by up to 80% and direct economic losses by up to 96%, while substantially decreasing population exposure from approximately 7,900 to 64 individuals.
These findings highlight the effectiveness of controlled overflows as a key element of residual flood risk mitigation, particularly when combined with conventional structural interventions. Such an approach enhances system adaptability and supports anticipatory, risk-informed floodplain management, representing a shift from passive flood defense toward proactive resilience-based planning.
How to cite: Domeneghetti, A., Dazzi, S., Mignosa, P., Vacondio, R., Colombo, A., and Martinengo, M.: Beyond Levees: Controlled Overflows for Managing Residual Flood Risk in the Enza River , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10263, https://doi.org/10.5194/egusphere-egu26-10263, 2026.
We introduce a high-resolution framework for evaluating climate-related risks at the building level, based on the IPCC risk model which conceptualizes risk as a function of vulnerability, exposure, and hazard. The framework focuses on pluvial flood risk, emphasizing impacts on residents’ well-being and mobility. The flood hazard is represented based on a 1-meter resolution hydrodynamic simulation of urban flooding triggered by a 100-year hourly rainfall event. Exposure is nuanced by impact type, considering ground-floor residents’ well-being and proximity to flooded streets affecting mobility and accessibility. Social vulnerability is quantified through socioeconomic indicators such as age, income, and education levels. Applying this framework to empirical data from Hamburg, Germany, we identify perilous hotspots where areas of high social vulnerability are combined with significant flood exposure. The framework was co-designed and tested with stakeholders from the city of Hamburg. To facilitate practical application also for other cities, we developed a Python-based ArcGIS toolbox for automated, building-level risk mapping. The framework’s transparent and adaptable design ensures broad transferability, to support local climate adaptation strategies and informed decision-making in urban resilience planning.
How to cite: von Szombathely, M., Vogelbacher, A., Lennartz, M., Poschlod, B., and Sillmann, J.: High-Resolution Framework for Urban Pluvial Flood Risk Mapping , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22154, https://doi.org/10.5194/egusphere-egu26-22154, 2026.
Sub-daily extreme precipitation can trigger severe flooding in urban catchments due to short hydrological response times. Although recent evidences show heterogeneous trends in magnitude and frequency across different regions of the world, rapid soil sealing from urban expansion – outpacing population growth – may significantly amplify pluvial flood risk. This study evaluates projected changes in pluvial flood risk for four megacities (population >10 million in 2010) under the Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP-RCP) 2-4.5 and 5-8.5 from 2020 to 2100. Megacities are selected globally based on geomorphic flood-prone areas, identified through digital elevation and floodplain datasets, and on population hotspots derived from historical gridded data. Pluvial flood hazard is assessed using a DEM-based hierarchical filling-and-spilling algorithm, and compared against detailed hydrodynamic modeling. Vulnerability assessment is conducted at present-day for simplicity, while a data-driven algorithm for predicting future building footprints associated with future demographic scenarios is under development. Results provide insights into how climate and urbanization interact to cast future pluvial flood risk in the world’s largest cities, informing adaptation strategies for sustainable urban planning.
How to cite: Spadoni, A., Traineau, A., Ceola, S., and Castellarin, A.: Pluvial Flood Risk in Megacities under Future Climate and Demographic Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12883, https://doi.org/10.5194/egusphere-egu26-12883, 2026.
Urban areas worldwide are increasingly exposed to hydro-meteorological extremes, including intense rainfall events and prolonged dry periods, which exacerbate flood hazards and water scarcity. Rooftop rainwater harvesting is widely discussed as a decentralised adaptation option that may contribute both to urban water supply and to the mitigation of hydrological extremes. However, existing assessments are largely limited to local case studies, and a consistent global-scale framework that links rooftop harvesting potential to hydro-meteorological hazard characteristics is still missing.
In this contribution, we present a global assessment framework to quantify the potential of rooftop rainwater harvesting using high-resolution building footprint data in combination with reanalysis-based precipitation datasets. The approach integrates detailed global building roof areas (LoD1) with ERA5-Land precipitation data for the period 2014–2024. Mean monthly precipitation climatologies are used to estimate long-term average harvestable water volumes, while daily precipitation data are considered to characterise precipitation intensity, seasonality, and temporal continuity relevant for flood and drought mitigation. Capture efficiency is applied to account for system-level losses.
By explicitly combining multiple precipitation timescales, the proposed framework enables a differentiated interpretation of rooftop rainwater harvesting potential under varying hydro-climatic regimes. While monthly precipitation provides a basis for estimating average water supply contributions, daily-scale metrics enable the assessment of conditions under which rooftop harvesting may be relevant for mitigating flood peaks or buffering dry spells. The study aims to provide a globally consistent, spatially explicit basis for evaluating rooftop rainwater harvesting as a complementary measure for increasing urban resilience to hydro-meteorological hazards.
How to cite: Chen, Y., Reimuth, A., and Zhu, X. X.: Towards a global assessment of rooftop rainwater harvesting for hydro-meteorological hazard mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18553, https://doi.org/10.5194/egusphere-egu26-18553, 2026.
The Intermountain Andean basins are characterized by complex topography and rapid peri-urban expansion. The central Paute River basin faces escalating threats from hydrogeomorphological hazards, particularly flash floods and landslides. Currently, in Ecuador, risk management strategies carried out by national and international institutions often lack high-resolution economic quantification of potential damages (Pinos & Timbe, 2020). This research bridges that gap by developing a multi-scalar methodology to quantify physical vulnerability and estimate economic losses, providing a critical tool for evidence-based land management.
This study integrates hydrogeomorphological hazard analysis with socioeconomic exposure modeling. The databases used are high-resolution digital elevation models from the Military Geographic Institute (SIGTIERRAS, 2014) and high-resolution drone surveys in identified active sectors that characterize the hazard (Torres Ramírez & Freire-Quintanilla, 2022). In contrast, this was coupled with microdata from the 2022 Census, provided by the National Institute of Statistics and Census (INEC, 2022), disaggregated to the census sector level. By applying a dasymetric mapping approach and cross-referencing building typologies with the 2025 Construction Price Index (IPCO) in Ecuador, we established a robust valuation framework for the building stock based on structural vulnerability and replacement costs.
The results reveal a distinct spatial correlation between high-vulnerability clusters and historical hazard events, particularly in the peri-urban periphery of the cantons Biblián, Azogues, Déleg, Paute, and Guachapala, which are among the cantons with the highest migration rates in Ecuador. These areas, defined by steep slopes and non-engineered masonry, exhibit the highest potential for economic loss. Conversely, consolidated urban centers demonstrate lower vulnerability despite high exposure density. This study indicates that integrating census-derived socioeconomic data into physical hazard models significantly refines risk estimation, offering a replicable framework for Disaster Risk Reduction (DRR) in the Andean region.
References:
INEC. (2022). Censo de Población y Vivienda. https://www.censoecuador.gob.ec/data-y-resultados/#pix-tab-398c8f9c-4977318
Pinos, J., & Timbe, L. (2020). Mountain Riverine Floods in Ecuador: Issues, Challenges, and Opportunities. Frontiers in Water, 2. https://doi.org/10.3389/frwa.2020.545880
SIGTIERRAS. (2014). Mosaicos de ortofotos a nivel nacional. Sistema Nacional de Información de Tierras Rurales e Infraestructura Tecnológica. Quito, Ecuador. https://bit.ly/2twJiRn
Torres Ramírez, R., & Freire-Quintanilla, K. (2022). Vehículos aéreos no tripulados en el análisis y monitoreo de eventos adversos en la zona centro de la cuenca del río Paute, Ecuador. XVII Coloquio Ibérico de Geografía, 312–331.
How to cite: Torres-Ramírez, L., Torres-Ramírez, R., and Marco-Molina, J. A.: Integrating a multi-scalar methodology to estimate vulnerability and economic losses for hydrogeomorphological risk assessment in the central Paute River basin, Ecuador, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15247, https://doi.org/10.5194/egusphere-egu26-15247, 2026.
Climate change, in combination with evolving development pathways, is contributing to increasing disaster risks globally. Understanding these risks requires the assessment of risk components, i.e., hazard, exposure, and vulnerability. Among them, social vulnerability is particularly challenging to assess due to its dynamic nature and the limited data availability in resource-constrained, high-risk countries for instance, Nepal. Existing studies in such regions often utilize open-source census data to assess vulnerability using a composite vulnerability index, but overlook spatio-temporal shifts in vulnerability and its components.
To address this gap, our study explores spatio-temporal disaster vulnerability in Nepal by applying Principal Component Analysis (PCA) to municipal-level population census data of 2011 and 2021. We applied PCA separately to individual vulnerability components of both years to identify changes in explanatory indicators. Then, we illustrate disaster vulnerability across Nepal for 2011 and 2021 and assess how it has changed over the decade. Finally, we investigate changes in central vulnerability components, namely, sensitivity and adaptive capacity.
The PCA reveals both continuity and transformation of drivers of sensitivity and adaptive capacity. Migration and literacy newly emerged in 2021 as principal components in sensitivity, while housing ownership and quality, as well as access to electricity, emerged in adaptive capacity. Overall, we observe a slight increase in the aggregated national vulnerability score, with approximately 45% of municipalities exhibiting high vulnerability classes in 2021. Most urban metropolitan cities and lowland regions (Terai) exhibit increased vulnerability, whereas Far Western regions witnessed a slight decrease in their vulnerability levels. A closer look at the shifts in sensitivity and adaptive capacity reveals that the increase in overall vulnerability was largely driven by a strong decrease in adaptive capacity in metropolitan cities and increased sensitivity in Terai regions. These findings suggest that focusing solely on composite vulnerability might lead to misguided mitigation strategies and that dissecting vulnerability into sensitivity and adaptive capacity offers actionable insights for decision-making. Furthermore, our approach supports multi-hazard risk and impact assessments in data-limited settings.
By investigating the temporal and spatial changes in vulnerability components, our study enhances the understanding of vulnerability dynamics in Nepal over the past decade, developing a refined approach for spatio-temporal index-based vulnerability assessments. To illustrate the potential applications of the findings in disaster risk management, we explored sectoral vulnerability interventions through key informant interviews with relevant authorities. Furthermore, our vulnerability assessment is being employed in a flood impact model that aims to identify the main drivers for reported flood fatalities in Nepal.
How to cite: Shrestha, A., Láng-Ritter, J., Chapagain, D., Taka, M., and Varis, O.: Spatio-temporal transitions of disaster vulnerability in Nepal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9601, https://doi.org/10.5194/egusphere-egu26-9601, 2026.
Flood susceptibility mapping plays a vital role in understanding and mitigating flood hazards, particularly in rapidly urbanizing regions where land-use and climate variability intensify runoff and exposure. Developing reliable susceptibility maps enables planners and decision-makers to enhance resilience, prioritize mitigation strategies, and design future-proof urban infrastructure. The Analytical Hierarchy Process (AHP) is widely applied in multi-criteria flood assessment as it provides a systematic framework to determine the relative importance of topographical and environmental factors affecting flood susceptibility. However, traditional AHP relies on expert judgment or values adopted from previous studies; these subjective weights vary across regions and reduce the accuracy and consistency of susceptibility zonation. The present study establishes a data-driven framework to improve AHP weight determination through machine learning and objective evaluation techniques. The coastal region along Jakarta Bay, Indonesia, which was severely impacted by the extreme flooding event of late December 2019 and early January 2020— caused by exceptionally intense monsoon rains and widespread surface runoff—was selected as the case study. Multiple geospatial layers were incorporated, including DEM, slope, curvature, aspect, TWI, TRI, SPI, STI, distance to river, NDVI, LULC, soil lithology, and rainfall frequency. Four complementary categories of methods were utilized to derive and refine AHP weights which include (1) probabilistic approaches (FR, WoE) and (2) statistical approaches (LR, GAM) and (3) objective weighting techniques (CV, Shannon Entropy, Entropy–CRITIC hybrid) and (4) machine-learning algorithms (RF, XGBoost, CatBoost, AdaBoost, SVM). The proposed hybrid framework enhances AHP objectivity through systematic integration of these methods which creates a solid base for flood susceptibility mapping in urban areas. The resulting susceptibility assessment show improved reliability, transparency, and spatial consistency, which enables planners to make evidence-based decisions for flood-risk management and long-term urban resilience development.
How to cite: Haddadi, A. and Safaie, A.: Hybrid Data-Driven and Enhanced AHP Framework for Flood Susceptibility Mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-611, https://doi.org/10.5194/egusphere-egu26-611, 2026.
Effective flood risk mitigation requires action at multiple levels. One key aspect is property-level flood risk management, which aims to decrease flood impacts on a local scale. Commonly, property owners bear the legal responsibility for flood prevention measures. However, about 30 percent of people in the European Union, including Sweden, are tenants who lack both the mandate and responsibility to carry out these measures since they do not own their homes. Instead, a landlord, often a company that rents out multiple housing units, is responsible for flood adaptation. In addition to the lack of mandate, tenants generally have fewer resources than homeowners and can therefore be more vulnerable to natural hazards, increasing the importance of landlord flood adaptation.
Despite the significant role of landlords in property-level flood management, their perceptions of flood risk and their strategies for implementing flood mitigation measures remain understudied, with previous studies mainly focusing on adaptation among homeowners or households in general. To fill this gap, we surveyed approximately 16% (95 respondents) of corporate landlords in Sweden regarding their perceptions of flood risk, attitudes toward flood mitigation measures, and views on responsibility for flood adaptation. The survey was designed using a combined framework of Protection Motivation Theory (PMT) and the Protective Action Decision Model (PADM).
The results of our survey show that nearly half of the landlords have experienced flooding, and more than half have taken precautionary measures such as acquiring pumps and improving drainage in and around properties. Yet most landlords also report a low perception of risk for future floods and believe that authorities have a significant responsibility for protecting properties as well. The interaction between landlords and tenants is limited, indicating that tenants may be vulnerable to future flood risks if landlords neglect their flood responsibilities. Our findings highlight the importance of incorporating landlords into broader flood risk management strategies to enhance protection for a large and vulnerable population.
How to cite: Schück, F., Arheimer, B., Mazzoleni, M., and Brandimarte, L.: Landlords’ Perceptions of Flood Risk and Adaptation Responsibility: Evidence from a Swedish Survey , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9350, https://doi.org/10.5194/egusphere-egu26-9350, 2026.
Extreme weather events such as tropical cyclones increasingly threaten societies as climate change amplifies their impacts. While climate risk assessments have traditionally focused on direct impacts, such as economic losses, population exposure, or mortality, post-disaster recovery remains largely absent from these frameworks, limiting our ability to assess long-term resilience.
This talk presents an approach to integrating recovery modeling into climate risk assessment using open-source, regional disaster recovery simulations that capture key dynamics such as resource constraints and interdependencies across systems.
Results reveal spatial disparities in rebuilding capacity relative to climate risks, highlighting where targeted policy and planning interventions could accelerate recovery and strengthen long-term resilience.
How to cite: Meiler, S., Blagojevic, N., Lochhead, M., and Baker, J. W.: Extending tropical cyclone risk assessment through recovery simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1559, https://doi.org/10.5194/egusphere-egu26-1559, 2026.
In recent years Oman and the MENA region have been significantly impacted by Tropical Cyclones (TC) which, on top of affecting the society in various aspects, have also led to unprecedented (re)insurance losses. Notable cyclones include Gonu (2007), Mekunu (2018), and Shaheen (2021). For instance, this last developed from the remnants of TC Gulab and made landfall on the coast of Al-Musannah, Oman, on 3rd October 2021 as a Category 1 Cyclone, while causing strong winds and heavy rainfall also around the capital city of Muscat and, in turn, deaths and widespread damage to both public and private properties. It is thus of increasing importance to accurately understand and reproduce TC risk in Oman. In general, this can serve to predicting and preparing for any event and, in the context of the (re)insurance industry, to avoid poor risk assessment and weak financial protection.
Reproducing and quantifying TC risk in Oman results still very challenging, mainly because it can be considered as an unmodelled country, i.e., it is not part of the domain of main catastrophe model vendors. In this study it is shown how Antares Global, under Qatar Insurance Company, the main insurance in the region, has faced this challenge in developing its own TC view of risk and catastrophe model for Oman. The study has mostly focused on the Wind component of the model, which consists of 10,000 years of stochastic catalogue relying on IBTracs data; a claim-based vulnerability module for main line of business, i.e., commercial, residential, and industrial, adjusted to four recent historical events; and a financial module that considers the conditional probability of having a loss, also in this case calibrated to the same recent historical experience.
It is shown how it has been possible to converge to a robust View of Risk with best combining the three main components of the model and adjusting them to the input exposure. Claim data has also required a detailed analysis to isolate the windstorm component from the flood and efficiently use it for the validation. Ongoing work is looking at expanding the framework to include the TC and extra tropical cyclones flood components.
How to cite: Scudeler, C., Richards, D., Kurien, J., and Carenzo, M.: Understanding and modelling Tropical Cyclone risk in Oman , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18066, https://doi.org/10.5194/egusphere-egu26-18066, 2026.
Melissa made landfall as a Category 5 major hurricane near New Hope, St. Elizabeth Parish in southwestern Jamaica on Tuesday, October 28 2025. It had maximum sustained winds of 295 km/h, and an accumulated precipitation exceeding 600 mm in most of the Caribbean island.
Moody’s RMS Event Response estimated private market insured losses from Hurricane Melissa to be between US$3 billion and US$5 billion. More striking, the total economic losses in Jamaica from this event are expected to be around one order of magnitude higher, and could potentially exceed the island’s GDP, which was approximately US$20 billion in 2024.
Several field reconnaissance surveys highlighted a dichotomy in Jamaica’s building stock between the insured and uninsured. Most insured buildings (in the industrial and commercial lines, e.g. hotels) are well-built, traditionally designed for seismic risk with concrete or reinforced masonry structures. In contrast, uninsured residential buildings largely exhibit less stringent build quality or enforcement of wind and flood design provisions, due in part to a lack of major hurricane landfalls since Gilbert in 1988. For example the flood insurance penetration in single-family dwelling is estimated to be as low as 7% in the island.
While the capital city of Kingston was largely spared from damaging winds, many other towns were devastated by a combination of catastrophic winds and widespread inland flooding. Being an island, repairs and recovery will inevitably go through significant supply chain challenges, even as several key ports on the island remain operational. For these reasons, recovery efforts are expected to take several months, if not years.
This analysis will explore the modelling behind the loss estimates presented, as well as the humanitarian catastrophe that this event represented for the general population, addressing the issues of the protection gap and building quality in the residential stock.
How to cite: Salinas Illarena, J. L., Khoury, S., Williams, J., and Hilberts, A.: Hurricane Melissa in Jamaica: humanitarian catastrophe and protection gap in residential buildings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19898, https://doi.org/10.5194/egusphere-egu26-19898, 2026.
We present an analytical framework for the space-time downscaling based on Bernoulli-lognormal (BLN, traditionally known as beta-lognormal) multiplicative cascades. Considering recent results about the analytical parametrization of the BLN generator, we derive the explicit relation for obtaining fine-scale statistics directly from the coarse-resolution inputs while preserving the space-time dependence structures characteristic multi-scale extreme precipitation. The method is implemented in an automated workflow on Google Earth Engine, which enters precipitation data in real time and dynamically updates the multifractal parameters to generate high-resolution space-time synthetic fields. We evaluate the performance of the scheme by comparing the disaggregated fields with independent observations. The results indicate that the procedure provides a robust approach for the downscaling of precipitation in hydrometeorological applications and supports improved occurrence probability estimation and uncertainty quantification for extreme events.
How to cite: Gaviria Arias, E., Hernández, C., Ruano, A., Villegas Cocone, I., and Carsteanu, A. A.: Downscaling of space-time rainfall using a Bernoulli-lognormal multiplicative framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-575, https://doi.org/10.5194/egusphere-egu26-575, 2026.
Assessing the statistical behavior of future extreme precipitation is a topical issue for the mitigation of pluvial and flood risk. There is increasing evidence that extreme short-duration precipitation is intensifying, but the quantification of such increase is still a challenging issue. Using one of the longest available daily precipitation series—continuously recorded in Bologna since 1 January 1813—we applied five extreme precipitation indices (Rx1day, R99p, R10mm, R20mm, and R99d) to evaluate the ability of 22 bias-corrected CMIP6 climate models in reproducing historical precipitation statistics. On this basis, we compared a dynamic weighted multi-model ensemble (DW-MME) based on multi-objective Pareto optimization with an equal-weighted multi-model ensemble (EW-MME) and individual models. We further assessed the performance of the DW-MME in projecting XXIst century changes under different emission scenarios. The results show that the DW-MME provides a substantially more robust and credible representation of extreme precipitation than both the EW-MME and single-model simulations. Under high emission scenario, future extremes exhibit a clear more extreme response, with the precipitation distribution shifting toward stronger and more extreme events, revealing a pronounced dependence on climate forcing.
How to cite: Lai, Y., Guo, R., and Montanari, A.: Extreme future precipitation in Bologna: an exploration based on different weighted multi-model ensemble methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1166, https://doi.org/10.5194/egusphere-egu26-1166, 2026.
Compound spatial precipitation events, occurring when extreme or moderate precipitation values manifest simultaneously or in sequence across multiple regions, amplify hydrological risks far beyond those of isolated events. This study assesses, at global scale, changes in compound spatial precipitation from 1980 to 2024, enabling the disentanglement of the individual contributions of spatial extent and intensity across regions. Our findings reveal that the expansion rate of the concurrent spatial area generally outpaces its intensification rate globally. This divergence is particularly pronounced in the tropical zone, suggesting that enhanced moisture supply in a warming atmosphere may be driving the increased spatial organization of extremes.
How to cite: Xing, T., De Michele, C., and Blöschl, G.: Global changes in Compound Spatial Precipitation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19249, https://doi.org/10.5194/egusphere-egu26-19249, 2026.
Agricultural production is highly sensitive to drought and extreme droughts are projected to increase globally in both frequency and severity. Agriculture accounts for more than 80 percent of drought related economic losses, estimated at USD 29 billion, globally. For subsistence farmers, timely financial assistance is critical to prevent prolonged income losses and worsening food insecurity. Parametric insurance helps address this need by triggering rapid payouts based on objectively measured and observed climatic conditions- rather than post-event loss assessments- thereby enabling faster and more predictable compensation.
This study develops a parametric insurance framework to protect vulnerable subsistence farming communities in Senegal against extreme drought and the resulting food insecurity. Agriculture contributes significantly to Senegal’s economy and employs a large share of the population, making the sector and population at large highly exposed to drought risk. The framework uses the Standardized Precipitation Evapotranspiration Index (SPEI) as the primary drought indicator, adjusted for vulnerable population density and crop-specific coefficients to better reflect water requirements across growth stages.The climatic variables used demonstrate a clear relationship with observed yield reductions during drought events. Different SPEI time scales (3, 6,12-months and combinations thereof) are tested against crop calendars and regional climatology to select the most suitable index structure for payouts triggering.
Payout structures are calibrated using historical yield data, food insecurity reports and estimates of affected populations to reduce basis risk. Ground validation and actuarial analysis strengthen the reliability of the index and its link to actual losses, thereby improving payout accuracy. This approach demonstrates the potential of parametric insurance as a scalable and practical tool for managing climate-related agricultural risks and supporting resilience among vulnerable farming communities.
How to cite: Kulkarni, S., Choudhary, S., Berraies, D., and Khagram, K.: Managing Drought Risk with Parametric Insurance: Addressing Food Insecurity in Senegal , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7580, https://doi.org/10.5194/egusphere-egu26-7580, 2026.
Landslides are among the most damaging natural hazards in Brazil. While impacts have long been concentrated in steep coastal mountain ranges, particularly in the Southeast, recent extreme rainfall events and expanding human occupation point to a broader and more complex national risk landscape. Because landslide occurrence is shaped by both hydro-meteorological forcing and land-use and settlement dynamics, a key question is how hydroclimatic shifts and territorial expansion interact with pre-existing susceptibility to shape hazard, exposure, vulnerability, and overall risk at the country scale.
Here we present a national-level assessment integrating: (i) landslide-susceptible terrain, (ii) geomorphometric controls, (iii) a spatial classification of hydrological-cycle tendencies, and (iv) population characteristics derived from census-based spatial units, together with indicators of spatial expansion. Susceptibility is represented through a nationalized interpretation of an existing global framework that combines topographic factors with proxies for geology, vegetation disturbance, and infrastructure. Terrain attributes are derived from elevation-based products, and hydroclimatic tendencies are summarized using a nationwide synthesis describing contrasting modes of hydrological-cycle change. All datasets are integrated at the census-tract scale, enabling direct comparisons among susceptibility patterns, hydroclimatic tendencies, and population distribution and expansion.
Results show that areas mapped as more susceptible often coincide with zones of higher human presence, indicating that exposure remains elevated where terrain conditions are unfavorable. In addition, vectors of population expansion frequently point toward more susceptible areas, which commonly include settlements with higher vulnerability. When hydroclimatic tendencies are intersected with the higher susceptibility classes, “drying” conditions appear more widespread, whereas “acceleration” occupies a smaller, yet still meaningful, portion of susceptible terrain. These patterns motivate two working hypotheses. First, in regions tending toward drying, a potential reduction in rainfall frequency or totals may lower landslide occurrence in typical years, but could also create conditions for larger responses during rare, high-intensity storms. Second, in regions tending toward hydrological acceleration, increases in rainfall intensity and/or event clustering are expected to promote more frequent triggering, consistent with observed behavior in well-known Brazilian hotspots.
Overall, this synthesis suggests that hydroclimatic tendencies may steer landslide regimes in different directions across Brazil, while continued settlement expansion increases exposure in susceptible terrain.
How to cite: Michel, G. P., Zanandrea, F., Fernandes, N., Teixeira, D., Cereto, A., Loureiro, R., and Cardoso, C.: Territorial expansion and hydroclimatic change as drivers of landslide risk in Brazil, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16266, https://doi.org/10.5194/egusphere-egu26-16266, 2026.
Translating global climate projections into decision-relevant information for climate adaptation is a critical hurdle for applied geosciences. This study presents a climate-informed landslide risk mapping framework developed for Taiwan, designed to bridge climate science with operational landslide risk management under climate change. Statistically downscaled daily precipitation projections from CMIP6 are employed to characterize future rainfall extremes, integrating them with geological susceptibility, bare land ratio, and population density to represent hazard, vulnerability, and exposure, respectively. Relative landslide risk is assessed using a quantile-based classification approach under Global Warming Levels (GWLs) of 1.5 °C, 2 °C, and 4 °C. To support applications across multiple decision scales, landslide risk maps are generated at 5 km grid resolution for regional-scale screening, at the township level for administrative planning, and at minimum statistical areas for detailed exposure assessment. The results demonstrate a consistent intensification of landslide risk with increasing global warming levels. Significantly, mountainous regions in northern and eastern Taiwan exhibit a nonlinear expansion of high-risk clusters under the 4 °C warming scenario, indicating heightened sensitivity to extreme precipitation changes. To explicitly address uncertainty in climate model projections, the framework incorporates a risk credibility indicator based on inter-model agreement, enabling a transparent interpretation of model robustness and avoiding deterministic use of climate projections. The framework has been operationalized through the Climate Change Disaster Risk Adaptation Platform (Dr. A), a web-based geospatial decision-support system that allows users to visualize landslide risk patterns across warming scenarios and to perform spatial overlay analyses with infrastructure datasets such as transportation networks and settlements. By providing multi-scale and scenario-based risk information, this study contributes a transferable methodology for integrating climate projections into landslide risk assessment and adaptation planning within regions.
How to cite: Chen, Y.-J., Lin, H.-J., and Liou, J.-J.: Bridging Climate Science and Adaptation Plan: Operationalizing Landslide Risk Management under Climate Change Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17656, https://doi.org/10.5194/egusphere-egu26-17656, 2026.
Abstract
Extreme precipitation events are among the most critical hydro-meteorological hazards in Italy, causing flash floods, landslides, and severe infrastructure damage. In 2023 alone, the extreme rainfall event that triggered floods and landslides in Emilia-Romagna caused 17 fatalities and 8.5 billion euros in damages (SNPA, 2024). While most studies generally focus on how the intensity of precipitation extremes is changing, the shift in their seasonality remains largely unexplored, such as the connection that can exist between these two characteristics. Indeed, extreme precipitation events are generally modulated by localized or large-scale weather conditions that can have a strong seasonal concentration. Moreover, the same precipitation amount can lead to markedly different consequences depending on when it occurs, due to different antecedent conditions (e.g., soil moisture, snowpack, etc.), making timing as important as intensity for risk assessment.
Italy’s complex morphology and climatic variability, from Alpine regions to Mediterranean coasts, lead to diverse seasonal patterns of precipitation extremes driven by atmospheric circulation, orography, and land–sea interactions (Mazzoglio et al., 2025). To the best of our knowledge, no systematic, nation-wide investigation across multiple sub-daily durations using historical rain gauge observations has been conducted to assess potential changes in the seasonality of extreme precipitation, also using intensity-related information.
The SEASONEX (a data-based investigation of the SEASONality of EXtreme rainfall in Italy) project aims to bridge this gap, delivering the first national characterization of the seasonality of extreme precipitation in Italy for durations ranging from 1 to 24 hours. The project is creating an extensive dataset of annual maxima dates by digitizing historical hydrological yearbooks and integrating recent observations from regional agencies, which are combined with magnitude information from the I2-RED database (Mazzoglio et al., 2020). This approach enables a multi-scale characterization of precipitation extremes, identifying predominant or multimodal seasonal concentration across the Italian territory. Beyond descriptive characterization, SEASONEX also investigates the spatial and temporal variability of seasonality. Innovative trend tests based on circular statistics are applied to detect non-stationarity and climate-driven shifts in seasonality, offering insights into how changing atmospheric conditions alter the timing of high-impact events. Finally, to advance risk understanding, the project employs circular–linear copulas to jointly model precipitation magnitude and timing (Treppiedi et al., 2025), enabling an assessment of out-of-season event probabilities.
Acknowledgments
Paola Mazzoglio and Dario Treppiedi gratefully acknowledge the Italian Hydrological Society for awarding the SEASONEX project the Florisa Melone Prize 2025.
References
Mazzoglio, P., Butera, I., & Claps, P. (2020). I2-RED: a massive update and quality control of the Italian annual extreme rainfall dataset. Water, 12(12), 3308.
Mazzoglio, P., Lompi, M., Marra, F., Dallan, E., Deidda, R., Claps, P., ... & Borga, M. (2025). Orographic and land-sea contrast effects in convection-permitting simulations of extreme sub-daily precipitation. Weather and Climate Extremes, 100798.
SNPA (2024). Il clima in Italia nel 2023. Report ambientali SNPA, n. 42/2024, Rome. https://www.snpambiente.it/wp-content/uploads/2024/07/Rapporto-SNPA-clima-2023.pdf.
Treppiedi, D., Villarini, G., Bender, J., & Noto, L. V. (2024). Precipitation extremes projected to increase and to occur in different times of the year. Environmental Research Letters, 20(1), 014014.
How to cite: Treppiedi, D., Mazzoglio, P., Noto, L. V., and Claps, P.: Exploring the seasonality of extreme precipitation in Italy: the SEASONEX project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-381, https://doi.org/10.5194/egusphere-egu26-381, 2026.
Weather generators are widely used in impact and risk assessment studies to produce long synthetic series of meteorological variables that reproduce current or future climate statistics and natural variability. Most stochastic weather generators are trained to well reproduce the bulk of the precipitation distribution, but they often fail to adequately represent extremes, leading to poor performance in flood hazard and hydrological risk applications. This limitation becomes particularly critical under climate change, as projected impacts on precipitation are expected to manifest differently for ordinary and extreme precipitation values. Here, we address this issue by integrating parametric Weibull tails estimated using the Simplified Metastatistical Extreme Value (SMEV) approach in ordinary weather generator series using a quantile mapping.
The methodology is tested using the AWE-GEN (Advanced WEather GENerator) model applied to a mountainous case study in Friuli Venezia Giulia (north-eastern Italy), characterized by a mean annual precipitation of ~1650 mm. The AWE-GEN implements the Neyman-Scott Rectangular Pulse (NSRP) model to reproduce the precipitation process. We generate 500 years of synthetic precipitation at 1 hour resolution for the current climate, and for the horizons 2050 and 2100 under RCP 4.5 and RCP 8.5 scenarios. To this end, we use EURO-CORDEX projections and the Clima Nord-Est platform to estimate the factors of change. Specifically, two different approaches are compared: a stochastic downscaling method implemented in AWE-GEN, which uses the EURO-CORDEX projections to assess the NSRP parameters for the future, and a simplified method that requires direct modification of the NSRP model parameters based on the expected factors of change. The parameters of the Weibull distribution for the future were obtained from transient simulations from a convection-permitting model (Lompi et al., 2025). The adopted downscaling methods led to significant changes in mean annual precipitation, mean annual number of events and mean intensity per event.
This research received funding from European Union NextGenerationEU – National Recovery and Resilience Plan (PNRR), Mission 4, Component 2, Investment 1.1 -PRIN 2022 – 2022ZC2522 - CUP G53D23001400006.
How to cite: Bassi, A., Marra, F., and Arnone, E.: Reproducing extremes in continuous stochastic precipitation series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9190, https://doi.org/10.5194/egusphere-egu26-9190, 2026.
Current flood risk estimates in Great Britain consider the impacts of climate change using uniform rainfall change factors, which fail to capture the spatiotemporal variability of short-duration, high-intensity rainfall that is vitally important for understanding surface water flood risk. The UKCP Local high-resolution (5 km, hourly) convection-permitting rainfall projections, with 12 ensemble members spanning 1980–2080, offer a unique opportunity to improve flood risk assessment in Great Britain. We developed a national-scale LISFLOOD-FP hydrodynamic model to spatiotemporally simulate 120,000 extreme rainfall events across Great Britain, examining how changes in short-duration rainfall influence surface water flood risk at the national scale and how these relationships evolve over time under climate change. We present the first comprehensive assessment of current and future changes in the frequency and severity of surface water flooding across Great Britain. Our results demonstrate the importance of explicitly representing spatiotemporal rainfall variability and its projected evolution in flood risk assessments, and highlight the value of an event-based approach for understanding current and future surface water flood risk in a changing climate.
How to cite: Archer, L., Devitt, L., Neal, J., Coxon, G., Bates, P., Kendon, E., and Bernie, D.: Event-based rainfall-driven flooding in Great Britain using Convection Permitting Models , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1983, https://doi.org/10.5194/egusphere-egu26-1983, 2026.
Floods caused by heavy precipitation events (HPEs) rank among the most damaging natural hazards. Under climate change, HPEs are projected to intensify in both spatial extent and rainfall magnitude. Yet extreme rainfall does not necessarily translate into extreme flooding because flood severity depends on the spatial coincidence of intense rainfall with catchments that have the hydrological properties to produce extreme floods. Such rare alignments may be poorly captured in historical observations, rendering conventional flood risk assessment, typically based on stream gauge records and extreme value analysis (EVA), inherently uncertain.
To address this uncertainty, counterfactual analysis - exploring alternative, hypothetical event scenarios - can help remove randomness in the spatial distribution of rainfall and reduce the element of surprise. Advances in precipitation monitoring, such as weather radar, together with increased computational capacity, now enable the systematic application of counterfactual approaches in flood risk management. This way the data basis can be artificially broadened. As a result, the method is gaining momentum in both the United States and Europe, supporting the development of more robust flood scenarios, also for ungauged catchments.
We introduce a framework to include counterfactual scenarios in conventional EVA for flood hazard assessments, with a particular focus on flash floods, and demonstrate that this approach substantially improves the anticipation of extreme floods. However, a central challenge lies in ensuring the physical plausibility of counterfactual scenarios. We therefore present and compare multiple methods for selecting counterfactual events and evaluate their influence on overall EVA-based hazard estimates. By identifying potential flood hotspots and reducing uncertainty, counterfactual thinking offers a valuable tool for disaster risk management, particularly in data-scarce regions.
How to cite: Voit, P., Fauer, F., and Heistermann, M.: Beyond Historical Records: Using Counterfactual Scenarios to Improve Flood Risk Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3126, https://doi.org/10.5194/egusphere-egu26-3126, 2026.
Climate change is altering the dynamics of groundwater fluctuations and posing new challenges for groundwater management worldwide. The decline in winter snow cover shifts precipitation infiltration more toward the winter season, while a prolonged vegetation period enhances evapotranspiration, leading to greater summer groundwater depletion. Extreme weather events such as floods and droughts, together with increasing water extraction driven by rising water demand, promote repeated cycles of drying and rewetting in near-surface, unconsolidated sediments. Over time, these cycles alter the hydromechanical properties of the subsoil and increase its susceptibility to deformation and subsidence.
In this study, we investigate these subsidence and deformation processes at historical monuments in central Germany, which have experienced pronounced structural damage. Since 2024, five observation sites of historic churches in the federal states of Saxony and Saxony-Anhalt have been monitored. These sites were selected because they are predominantly located in rural regions, where groundwater systems are comparatively less affected by urban-related stressors, allowing climate-related groundwater fluctuations to be examined with reduced interference from superimposed anthropogenic signals. The monuments were constructed several centuries ago and have remained largely stable over time. However, after several years of extreme weather conditions, significant cracks began to appear around 2016. In some cases, the buildings were temporarily classified as being at risk of collapse. Since the damage did not occur immediately following individual extreme events but developed over an extended period, the long-term trend in subsurface water saturation needs to be investigated. To distinguish persistent drying trends from seasonal fluctuations, quarterly electrical resistivity tomography (ERT) measurements were conducted in the vicinity of the monuments along fixed profiles with lengths of up to 160 m during six field campaigns between April 2024 and November 2025. During the observation period, the electrical resistivity in the shallow subsurface increased significantly, indicating progressive desiccation to a depth of approximately 5 m, with wintertime rewetting insufficient to restore moisture levels. This prolonged desiccation likely induced further shrinkage and deformation, especially in the clay-rich layers. In contrast, a decrease in electrical resistivity was measured in the deeper layers, indicating a higher moisture content compared to the drier upper soil layers. Continued monitoring will further contribute to determining the long-term effects of climate variability on subsurface moisture dynamics, delineating zones with critical moisture changes, and linking these to settlement-prone areas of the monuments.
How to cite: Lehmann, W., Römhild, L., Gossel, W., and Bayer, P.: Groundwater-driven land subsidence as an emerging risk to historical monuments in central Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11097, https://doi.org/10.5194/egusphere-egu26-11097, 2026.
Abstract
Understanding the spatio-temporal evolution of compound hydro-climatic extremes is critical for assessing climate-related risks in monsoon-dominated river basins. This study examines long-term changes in rainfall and temperature extremes across the Damodar River Basin, India, using station-based extreme climate indices derived from daily observations. Fifteen meteorological stations representing diverse physiographic and climatic conditions within the basin were analyzed to capture spatial variability and temporal evolution of hydro-climatic extremes.
A comprehensive suite of rainfall-based indices (CDD, CWD, PRCPTOT, R10mm, R20mm, R95p, R99p, RX1day, RX5day, and SDII) and temperature-based indices (TNn, TNx, TXn, TXx, and DTR) was employed to characterize changes in the frequency, intensity, and persistence of extreme events. Monotonic trends in individual indices were assessed using the non-parametric Mann–Kendall test, while Sen’s slope estimator was applied to quantify the magnitude of change. Statistical significance was evaluated at the 95% confidence level, ensuring robustness against non-normality, outliers, and data heterogeneity commonly associated with hydro-climatic time series.
To investigate compound behavior, rainfall and temperature extremes were jointly interpreted within the framework of hot–wet, hot–dry, and wet–cold event combinations. Station-wise comparisons of trend direction and magnitude were used to identify spatial patterns and emerging hotspots of compound hydro-climatic extremes across the basin. The results reveal pronounced upstream–downstream contrasts and substantial regional heterogeneity in the evolution of compound extremes, reflecting the combined influence of monsoon dynamics, topographic variability, and local climatic conditions.
The proposed framework offers a systematic and data-efficient approach for analyzing the spatio-temporal evolution of compound hydro-climatic extremes using observed climate indices. The findings provide valuable insights for basin-scale climate risk assessment and support informed decision-making related to water resources management, infrastructure resilience, and disaster risk reduction in monsoon-affected river basins.
Keywords
Compound hydro-climatic extremes; Extreme climate indices; Trend analysis; Spatio-temporal variability; Damodar River Basin.
How to cite: Verma, A., Pandey, K. K., and Kumar, S.: Spatio-Temporal Evolution of Compound Hydro-Climatic Extremes in a Monsoon-Dominated River Basin in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16376, https://doi.org/10.5194/egusphere-egu26-16376, 2026.
The present study investigates the firm-level impacts of Italy’s recently enacted compulsory insurance law for natural disasters (Law No. 213 of December 30, 2023), with a focus on flood risk. By disaggregating firm data and classifying it by sector, the study compares the number of insured firms with catastrophic (natural hazard) insurance and the total value of insured assets before and after the policy was implemented. Before the reform, the data reveal a market structure in which insurance coverage was held mainly by larger firms, with most SMEs remaining uninsured. The post-policy scenario indicates a substantial structural shift, with near-universal insurance penetration expected among SMEs and a significant expansion in the total insured asset base, despite insured firms increasing at a much faster rate than insured values.
This study also analyzes the various insured values of assets by sector, firm size, and flood hazard zones throughout Italy. Using flood hazard maps, a spatial analysis highlights approximately 1.13 million firms located in areas with varying levels of flood risk. These findings provide a preliminary overview of the expected changes in insurance penetration and geographic exposure resulting from the reform. However, a comprehensive assessment of the reform’s effectiveness in enhancing resilience and reducing risk remains a complex and ongoing challenge that requires further empirical investigation.
How to cite: Kale, C., Martina, M. L. V., Dottori, F., and Sepetoglu, M.: Attempts to Close the Protection Gap: Preliminary Evaluation of Italy's Compulsory Disaster Insurance Reform for Firms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14820, https://doi.org/10.5194/egusphere-egu26-14820, 2026.
Despite the growing abundance of precipitation datasets, the availability of high temporal and spatial resolution observations from rain gauges is still limited and fragmented. However, these data are essential especially when the focus is on intense precipitation, since other products (e.g., satellite, radar, and reanalysis) may be affected by important biases.
In Italy, hourly precipitation measurements are managed independently by regional or sub-regional institutions, resulting in the absence of a unified national-scale dataset. To address this gap, we present the first comprehensive hourly precipitation database for Italy, obtained by integrating observations from ~ 3,000 continuously monitoring rain gauges. The database spans several decades, with some time series beginning in the early 1980s, while the highest spatial coverage is achieved from the early 2000s up to 2024. An extensive pre-processing phase was carried out to standardize and organize the dataset, e.g., by removing duplicate stations and standardizing the coordinates and the timing to a common reference system. To ensure data reliability and consistency, a comprehensive quality control procedure was also applied, by adapting to the specific characteristics of the Italian climate a set of well-established methodologies from the literature (e.g., Blenkinsop et al., 2017, Lewis et al, 2021). Quality control was designed to identify and correct common issues such as the erroneous aggregation of daily totals into single hourly records, outliers (detected using statistical thresholds based on observed data extremes), and unrealistically high values occurring after prolonged data gaps, usually indicative of sensor malfunction.
The resulting dataset represents a robust basis for a wide range of applications. For instance, it allowed us to characterize how intense precipitation is distributed across the Italian territory in terms of magnitude and seasonality, and to further investigate the diurnal cycle of extreme rainfall. Another key outcome concerns the probabilistic analysis of extreme precipitation. Although the temporal extent of the dataset is not adequate to support analyses based on classical extreme value theory, it can be analyzed with more effective recent approaches, such as the MEV (Marani & Ignaccolo, 2015) and the SMEV (Marra et al., 2019) frameworks. Finally, beyond research applications, the dataset offers a valuable support for risk management, adaptation planning, and infrastructure design under changing climate conditions.
Acknowledgments
This research received funding from European Union NextGenerationEU – National Recovery and Resilience Plan (PNRR), Mission 4, Component 2, Investiment 1.1 - PRIN 2022 – 2022ZC2522 - CUP G53D23001400006.
References
Blenkinsop, S., Lewis, E., Chan, S. C., & Fowler, H. J. (2017). Quality‐control of an hourly rainfall dataset and climatology of extremes for the UK. International Journal of Climatology, 37(2), 722-740.
Lewis, E., Pritchard, D., Villalobos-Herrera, R., Blenkinsop, ... & Fowler, H. J. (2021). Quality control of a global hourly rainfall dataset. Environmental Modelling & Software, 144, 105169.
Marani, M., & Ignaccolo, M. (2015). A metastatistical approach to rainfall extremes. Advances in Water Resources, 79, 121-126.
Marra, F., Zoccatelli, D., Armon, M., & Morin, E. (2019). A simplified MEV formulation to model extremes emerging from multiple nonstationary underlying processes. Advances in Water Resources, 127, 280-290.
How to cite: Noto, L. V., Treppiedi, D., Sanchez Pena, C. A., Darienzo, M., Ezeaba, A., Khan, U., Paranunzio, R., Francipane, A., Arnone, E., Marra, F., and Marani, M.: A quality-controlled hourly precipitation dataset for the analysis of intense precipitation over Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17901, https://doi.org/10.5194/egusphere-egu26-17901, 2026.
The frequency of underground flooding has been increasing due to the intensification of extreme rainfall events and rapid urbanization. Three-dimensional (3D) CFD simulations enable the analysis of complex flow behaviors that are difficult to capture using two-dimensional (2D) models, particularly in areas with large hydraulic gradients, where turbulent and vortical flows frequently occur. In addition, the CFD simulations allow for detailed representation of structural effects, including buildings, underground facilities, and flood protection structures such as flood barriers.
In this study, an underground parking facility within a multi-use building is selected as a case study to analyze flood hydraulics in underground spaces. The flooding process is analyzed in both spatial and temporal dimensions to identify the onset time of inundation and the progression of flood depths. Based on this analysis, evacuation times are estimated to support decision-making for emergency response and flood risk management in underground facilities.(This research was supported by a grant(RS-2025-02313776) of the Regional Customized Disaster-Safety R&D Program funded by Ministry of Interior and Safety(MOIS, Korea).)
How to cite: Um, J. A., Lee, S., and Lee, S.: Analysis of Underground Flooding Phenomena and Decision Support Using 3D CFD Simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16976, https://doi.org/10.5194/egusphere-egu26-16976, 2026.
Design Flood (DF) inputs for large reservoir systems, such as Intensity-Duration-Frequency (IDF) curves and Probable Maximum Precipitation (PMP), are traditionally derived under stationarity assumptions, which are increasingly challenged under a changing climate. The current study examines changes in extreme rainfall characteristics across ten important dams in the Godavari River Basin (GRB), India, under three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Daily rainfall projections from the NEX-GDDP-CMIP6 dataset are evaluated against gridded observations of the India Meteorological Department (IMD) for the historical period (1951-2014). Nine statistical performance metrics, combined with five Multi-Criteria Decision-Making (MCDM) methods and a Group Decision-Making (GDM) framework, are used to identify the best-performing (top-five) Global Climate Models (GCMs). Based on this evaluation, five GCMs – BCC-CSM2-MR, CMCC-ESM2, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NESM3 are selected for GRB. Next, non-stationarity in extreme rainfall is assessed using epoch-wise analysis, trend detection methods (Mann-Kendall test and Sen’s slope estimator), and a change-point detection technique (Pettitt’s Test). The results of statistical analyses show significant increases in short-duration rainfall extremes in recent decades. Subsequently, IDF curves are developed for multiple return periods (100-, 200-, 500, and 1000-year) using the Gumbel distribution (GEV-1). The results revealed a robust intensification of short-duration rainfall extremes under future climate scenarios, with SSP5-8.5 exhibiting the largest increases, implying that stationary design assumptions may underestimate future dam safety risks. Furthermore, PMP is estimated using the Hershfield method, and the results indicated increases ranging from 8.55% to 44.11% across the selected dam locations. Overall, the study underscores the necessity of revisiting stationary design assumptions and offers a scalable framework for climate-resilient design storm estimation for large reservoir systems. While increases in PMP are evident, their direct application without field-level validation may lead to over- or under-conservative design decisions. Hence, future work should focus on reconciling model-based PMP estimates with observed extreme events, local meteorological records, and dam-specific field conditions, alongside hydrological and reservoir routing analyses, to support robust and reliable dam safety assessments.
Keywords: Climate Change, Non-stationarity, Intensity-Duration-Frequency (IDF), Probable Maximum Precipitation (PMP), NEX-GDDP-CMIP6 models
How to cite: Roulo, D., Nakka, N. K., Mansuri, I., and Pichuka, S.: Climate Change Induced Extreme Rainfall and Its Impacts on Large Reservoir Systems: A Non-Stationarity Perspective, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18165, https://doi.org/10.5194/egusphere-egu26-18165, 2026.
Floods represent the most common type of natural disaster worldwide, resulting in devastating socio-economic impacts. While much research has been conducted on flood impacts in the Global North, much less is known about how these impacts vary across regions with different economic and social conditions. Moreover, little is known about how measured impacts compare with public perception of flood risk, which is relevant for how populations respond to flood risk management measures. This study has two main objectives: 1) to quantify and compare flood impacts within and between the European Union (EU) and the Middle East and North Africa (MENA) region using the Emergency Events Database EM-DAT and 2) to compare the recorded impacts with the public perception of flood risk within the EU with the results from the SP547 Eurobarometer survey. More floods were recorded in the EU, and they caused economic losses that were almost two times more important as a proportion of GDP. However, human impacts were nearly four times greater in the MENA region. The seasonality of floods and of their impacts varies strongly across regions, being more prevalent in summer in central and eastern Europe, in autumn in the western Mediterranean, and in autumn and winter in the eastern Mediterranean. The comparison between recorded impacts and public perceptions shows that flood risk is overestimated by the population in northern EU countries and underestimated in southern EU countries. Our results highlight the need for improved flood impact and flood perception data to facilitate flood research, especially in the MENA region where available data is limited yet the population is greatly impacted by flood disasters.
How to cite: Coleman, M., de Vries, A.-J., Roberts, C., and Domeisen, D. I. V.: Socio-economic impacts, characteristics, and perception of floods in the European Union and the Middle East and North Africa region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19735, https://doi.org/10.5194/egusphere-egu26-19735, 2026.
An increase in globally occurring extreme precipitation events during recent decades has led to catastrophic floods, a trend projected to intensify in the future. In Spain, this issue is particularly critical due to the irregular and convective nature of Mediterranean precipitation and the high exposure of populated and agricultural areas, as well as transport infrastructure. However, the scarcity of long-term observational records limits our understanding of past flood variability and recurrence. Here we present a comprehensive analysis of historical floods in the Mediterranean basins of the Iberian Peninsula based on historical documentary records. The dataset spans from 1035 to 2020 CE and compiles 14,417 individual flood cases, grouped into 4,394 flood episodes, each characterized by location, geographic coordinates, river basin, and affected rivers. Additional information includes impacts on fluvial systems and infrastructure, classified by impact intensity, and in many cases, precise temporal resolution (day, month, year). Although the dataset represents a partial reconstruction of past reality, its magnitude provides robust insights into long-term flood dynamics. Spatial analyses reveal that events can range from basin-restricted to large multi-basin episodes extending from the Andalusian Mediterranean to the Ebro basin. Event duration varies widely and is not always correlated with spatial extent. From a seasonal perspective, most floods occur in autumn, though intense summer and spring floods are also recorded, the latter often linked to snowmelt in the Pyrenees and other mountain ranges. While a long-term increase in flood occurrence is observed, with a marked peak in the most recent decades, interpretations of recurrence variability must be made cautiously, as the record also reflects changes in exposure, increasing social impacts, and improvement in reporting capacity over time. This study constitutes a solid foundation for exploring hydroclimatic variability, societal vulnerability, and the evolving human–environment relationship over the last millennium.
How to cite: Rodriguez Morata, C., Lloberas-Milan, G., Molowny-Horas, R., David, P., Tuset, J., Balasch, C., Barriendos, J., Ummenhofer, C., Barriendos, M., and Andreu-Hayles, L.: A long-term perspective of floods in the Spanish Mediterranean Basins from historical archives (1035–2020 CE), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22308, https://doi.org/10.5194/egusphere-egu26-22308, 2026.
In September 2024, a record rainfall of up to 300 to 400 mm, or even more, fell in northeastern Austria just within five days, leading to massive floods that significantly surpassed a 100-year flood event. In the future, climate change will further increase the frequence and intensity of flooding, making the reduction of risk and damage from floods a continuing challenge. Assessing and understanding social, economic, and environmental vulnerability, alongside resilience, is therefore crucial to strengthening the adaptive and mitigation capacities of communities. Vulnerability is defined as a function of sensitivity, susceptibility, and capacity to cope and adapt. From this perspective, vulnerability describes the tendency or predisposition of exposed elements to suffer adverse effects from flooding. It is determined by physical characteristics of buildings and infrastructure, as well as social, economic, institutional, and environmental conditions that influence the capacity of individuals, households, and communities to anticipate, cope with, and recover from floods.
Knowledge of flood hazards and exposure has improved significantly in recent years. However, the assessment of vulnerability remains a major challenge. Detailed insights on municipality level are needed to evaluate and improve current protection measures for residents and mitigation strategies. Therefore, it is important to understand how vulnerability relates to flood impacts not only theoretically but also practically. In this study, we conduct a pre-event vulnerability assessment of Austrian municipalities affected by a major flood event in 2024 and evaluate if lower vulnerability correlates with a lower impact (e.g. fewer affected buildings and infrastructure, lower economic damage), and vice versa.
The exposure, susceptibility, and resilience of affected communities will be analysed to create an indicator-based vulnerability index. Based on a literature review, a set of indicators will be defined, including socio-economic (e.g. age, income), physical (e.g. proximity to rivers, elevation) and other (e.g. accessibility to health services, land use) data. The indicators are normalized and statistically weighted using machine learning techniques, such as regression analysis or random forest. The flood extent will be derived from the Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) satellite data. Geospatial data will be used to obtain for example, accessibility, land use data and statistical data will be used for obtaining socio-economic or demographic information per municipality. Finally, the calculated flood vulnerability index will be evaluated by comparison with observed flood impacts, SAR-derived flood extent, as well as official flood risk maps.
Our findings will improve the understanding of the factors influencing the vulnerability of communities to floods and how vulnerability is linked to the impact of major flood events in Austria. The results can support policymakers in formulating recommendations for those responsible for flood risk management at the municipal level.
How to cite: Streifeneder, V., Dabiri, Z., Hölbling, D., Adamiak, M., Borowska-Stefańska, M., Wiśniewski, S., and Magiera, M.: Linking vulnerability and impact of floods in Austria – A case study of the flood events in 2024, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21677, https://doi.org/10.5194/egusphere-egu26-21677, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot A
EGU26-20263 | ECS | Posters virtual | VPS8
Controls and Predictability of Large Floods in the Brahmaputra River BasinTue, 05 May, 14:54–14:57 (CEST) vPoster spot A
The Brahmaputra River Basin is among the most flood-prone regions globally, experiencing recurrent large floods with severe socio-economic and ecological impacts. Despite extensive flood management interventions, forecasting skill remains limited due to the basin’s complex hydrology, strong monsoon variability, and pronounced land–atmosphere interactions. This study investigates the drivers and dynamics of large floods in the Brahmaputra Basin, with a particular emphasis on coupled land–atmosphere processes. We conduct a composite analysis of major flood events using reanalysis datasets, satellite observations, and hydrological records. Our results show that large floods are consistently associated with anomalously high atmospheric moisture content, extreme and spatially extensive precipitation, and elevated antecedent soil moisture that amplifies runoff generation. The concurrence of saturated catchments with persistent multiday monsoon rainfall leads to rapid escalation of flood magnitude and prolonged flood duration. In addition, enhanced moisture transport into the basin emerges as a critical contributor to the development of large flood events. By integrating these insights into coupled land–atmosphere modeling frameworks, we demonstrate that improved representation of soil moisture dynamics, rainfall persistence, and moisture transport pathways can substantially enhance flood predictability. This work advances the understanding of flood-generating mechanisms in monsoon-dominated river basins and provides actionable insights for improving early warning systems and adaptive flood risk management in the Brahmaputra Basin.
How to cite: Vangala, G. and Mishra, V.: Controls and Predictability of Large Floods in the Brahmaputra River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20263, https://doi.org/10.5194/egusphere-egu26-20263, 2026.
