The estimation, attribution or projection of hydro-meteorological extremes in individual locations is constrained by the limited number of observations of extreme events. Recent advances in large-sample machine learning (ML) models, however, have demonstrated significant potential to mitigate the impact of data scarcity on the quantification of hydrological risks. These models integrate hundreds to thousands of time-series records alongside local descriptors of climate and catchment characteristics, enabling them to learn relationships across diverse environments and provide accurate estimations of hydro-meteorological extremes. This presentation will highlight our recent advancements and challenges in developing large-sample ML models for estimating, attributing, and projecting hydro-meteorological extremes.
At the core of our ML models is the GRIT river network, a new global bifurcating network which includes multi-threaded rivers, canals, and deltas. Unlike conventional single-threaded global river networks, GRIT incorporates bifurcations derived from the 30m Landsat-based river mask from GRWL and elevation-based streams from the FABDEM digital terrain model. This realistic depiction is critical, as 98% of floods identified in the Global Flood Database occur within 10 km of a river bifurcation. Individual river reaches in GRIT are assigned a broad range of static and time-varying variables describing the local meteorology, climate, geology, soils, geomorphology, Earth observation, terrestrial water storage, land cover time series, socio-economic data, and a novel archive of historical river discharge records from approximately 60,000 gauges.
This novel dataset enables us to tackle three key challenges: (1) Flood estimation: We estimate flood hazards globally, such as bankfull river discharge, the mean annual flood, and return periods, and assess the ability of the models to produce spatially-consistent hazard estimates. By leveraging an expanded training envelope, the ML models generate reliable estimates in data-sparse regions. (2) Flood attribution: Leveraging a range of explainability methods such as model probes, sensitivity testing, SHAP, ALE, PDP, and gradient-based methods, we investigate flood-generating mechanisms across diverse catchment types. Explainable AI (XAI) tools enable us to interrogate the models to enhance our understanding of the physical and anthropogenic drivers of flooding. (3) Flood prediction and projection: We assess the utility of hybrid large-sample ML models trained directly on subseasonal to seasonal forecasts or Earth system model (ESM) outputs for future flood projections. We show how large-sample models can implicitly correct spatio-temporal biases in forecasts or ESM outputs and deliver reliable predictions, bypassing traditional modelling steps such as downscaling and bias-correction.
Finally, we discuss key challenges in large-sample modelling, such as systematic biases in training data, inconsistencies in XAI results, causality, and the relative strengths and weaknesses of simple ML models versus deep learning. These challenges underscore the need for continued innovation in large-sample model design and application. By integrating diverse datasets and advanced ML techniques, large-sample models present transformative opportunities for flood estimation, attribution, and projection, enabling informed decision-making for management of hydro-meteorological extremes.
How to cite: Slater, L., Wortmann, M., Moulds, S., Liu, Y., Zhang, B., Hawker, L., Deng, L., and Ford, E.: Large-Sample Machine Learning Models for Estimation, Attribution, and Projection of Hydrometeorological Extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6432, https://doi.org/10.5194/egusphere-egu25-6432, 2025.
Determination of the reliable estimate of risk associated with hydrometeorological extremes over a region requires discerning information on spatial variability of the associated at-site statistics/parameters. Extreme rainfall at finer spatio-temporal resolution allows for improved analysis of spatial variability, as local-scale statistical similarities (LSS) and heterogeneities are disclosed. The knowledge of LSS facilitates the use of information on regional spatial variability (in lieu of complex at-site spatial variability) for risk analysis. In addition, it is established in literature that geographical features influence the occurrence of extreme rainfall over an area. For a subcontinent with complex non-uniform patterns of geographical features, the regional spatial variability may be influenced by the geographic composition. To quantify this regional spatial variability, statistically homogenous regions need to be deciphered. Most studies on the regionalization of sub-daily extreme rainfall (SDER) are limited to a smaller spatial extent, and none was focused on a subcontinent. Furthermore, there are no prior studies focused on the analysis of regional spatial variability of SDER. To study the role of geography in modulation of the regional spatial variability of mesoscale SDER, the present study proposes a framework. It involves (i) dividing the study area into subareas based on geographical features, as they are deemed to influence the occurrence of extreme rainfall, (ii) the delineation of each subarea into statistically homogenous SDER regions using a novel regionalization technique, (iii) quantification of the regional spatial variability of SDER in each subarea using the delineated regions and a proposed novel index, and (iv) identifying the role of geographic features in modulating the regional spatial variability. The efficacy of the proposed framework is demonstrated by application to Indian subcontinent (66.5-100o E, 6.5-38.5o N) considering 0.12o resolution SDER data corresponding to different durations (1,2,3,6 and 12-hour) for the period 1981-2020. The data were prepared by bias correcting the 0.12o resolution NCMRWF IMDAA hourly gridded rainfall (at 20,717 grids) to be consistent with the widely used 0.25o resolution IMD (India Meteorological Department) daily rainfall. The Indian subcontinent is divided into seven subareas based on geographic features. On application of the framework, it has been found that the regional spatial variability of SDER in a subarea is regulated by its geography and that of its neighbouring subareas. Insights are obtained on the effect of factors such as orography and coastal width on regional spatial variability of SDER. The study is of significance as the knowledge discerned on potential covariates/attributes has wide applications including identification of similar extreme rainfall sites for regional frequency analysis for extreme rainfall and risk assessment of consequent floods at ungauged/sparsely gauged hotspots such as water control (e.g., dams, barrages, levees) and conveyance infrastructure (culverts) in river basins under various climate change scenarios. The inherent physio-geographic features of the catchment may not be enough to analyze the similarity with neighbouring catchments. The boundary conditions around the catchment also plays a role.
How to cite: Varshney, A. and Srinivas, V. V.: A New Framework for Quantification of Regional Spatial Variability of Mesoscale Sub-daily Extreme Rainfall for Subcontinent , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1068, https://doi.org/10.5194/egusphere-egu25-1068, 2025.
Extreme precipitation events, characterised by significant rainfall amounts over short periods, are projected to intensify and occur more frequently under the influence of climate change. These projected changes, combined with rapid urbanisation, will likely lead to more frequent and extreme pluvial flood events (urban and flash floods) due to the precipitation intensity rapidly and easily exceeding the current capacity of natural and artificial drainage systems. Assessing the impact of future climate scenarios on extreme precipitation is therefore critical for identifying and designing sustainable adaptation and mitigation actions for at-risk communities and their citizens.
As part of the EU Horizon 2020 project CLIMAAX, an extreme precipitation workflow has been developed to provide step-by-step guidelines for communities and regions to identify and assess how their critical rainfall thresholds could shift in both magnitude and frequency under climate projections. In this work, a critical rainfall threshold is defined as the precipitation intensity necessary to trigger unsustainable or unacceptable impacts in a specific location or area. These thresholds are commonly used in designing drainage systems and flood protection infrastructure and serve as decision support values for triggering rainfall warnings or advisory information during emergencies. By employing the workflow to assess how these critical rainfall thresholds are projected to change, communities can make informed decisions about the most appropriate long-term adaptation measures to enhance their overall climate resilience. Moreover, the flexible workflow structure facilitates the integration of diverse hazard, exposure and vulnerability datasets at multiple scales (e.g., CORDEX, WorldPoP), making it adaptable to specific regional needs.
The extreme precipitation workflow has been applied in the Catalonia Region, Spain, to evaluate how the current rainfall thresholds used for triggering rainfall warnings for Dangerous Meteorological Situations will vary due to the influence of climate change. Model combinations of EURO-CORDEX climate projections at a 12km spatial resolution for the different Representative Concentration Pathways (RCPs) were employed for assessing future rainfall projections. Considering the increased number of extreme precipitation events in the region over the past years, the impacts associated with these and the number of triggered warnings per year, the results are expected to provide authorities with valuable insights into the frequency and magnitude shifts of these extreme events in the region.
How to cite: Meléndez-Landaverde, E., Sempere-Torres, D., González, V., and Corral, C.: Are rainfall warning levels ready for climate change? A case study from Catalonia, Spain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10418, https://doi.org/10.5194/egusphere-egu25-10418, 2025.
Classical extreme value analysis (EVA) often give large uncertainties on estimated return levels due to the limited length of real-world hydrological time series. The metastatistical extreme value (MEV) approach (Marani and Ignaccolo 2015) aims to overcome these limitations by describing all data using a common distribution, treating extremes as large ordinary data values. The above authors perform Monte Carlo simulations with synthetic time series generated from a Weibull distribution and fit a Weibull distribution to each series, as prescribed in the MEV approach. These simulations show that the MEV give unbiased estimates with smaller confidence intervals, compared with the GEV and Gumbel methods from classical EVA.
However, the MEV method neglects that physical mechanisms producing extremes often differ from those for ordinary events. Therefore, the ordinary and extreme events should in general be described by a mixture distribution and this may influence the results of MEV. To test this, we replicated their work and added a variant using synthetic time series from a Weibull mixture distribution, formed by mixing the original Weibull distribution with a tiny fraction of another Weibull distribution with a longer tail. This mimics the shift in distribution between ordinary and extreme events. When applying the Weibull-based MEV to the Weibull mixture samples, the MEV method produced systematically biased estimates, which are outside the confidence intervals provided by MEV. In contrast, GEV produced unbiased estimates that are inside the confidence interval.
Finally, goodness-of-fit tests are not able to distinguish between time series distributed according to Weibull and Weibull mixture, and can therefore provide no guidance on when to use MEV. In summary, we find the MEV approach unreliable for real-world applications and strongly caution against using it.
How to cite: Schmith, T., Arnbjerg-Nielsen, K., and Christiansen, B.: The Risk of Negatively Biased and Overconfident Return Level Estimates: A Critique of the Metastatistical Approach to Extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6777, https://doi.org/10.5194/egusphere-egu25-6777, 2025.
Lightning poses a significant threat to life, infrastructure, and economic sectors worldwide. This study evaluates lightning risk at the municipal level in Mexico by integrating the interplay of natural hazards and social vulnerability into a comprehensive risk estimation. Although lightning-related fatalities have declined in Mexico, likely driven by demographic shifts and improved urban infrastructure, significant social vulnerability persists, particularly in rural areas where labor-intensive agriculture and lower education levels are prevalent. Using this integrated approach, we develop a lightning fatality risk map that identifies high-risk regions in Mexico. These regions are characterized by high lightning occurrence and elevated social vulnerability. By providing detailed municipal-level insights, this research contributes to advancing local resilience and informing policy and disaster risk mitigation efforts, ultimately enhancing public safety in the face of natural hazards.
How to cite: Jaramillo, A. and Dominguez, C.: Integrating natural hazards and social vulnerability to estimate lightning-related mortality risk in Mexico, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3385, https://doi.org/10.5194/egusphere-egu25-3385, 2025.
The 2024 hurricane season presented unique challenges in hydrological and risk modeling with the consecutive landfalls of Hurricanes Helene and Milton in Florida, USA. This study investigates the compounded, cascading, and multihazard perils associated with these events, focusing on the interplay of antecedent conditions, vulnerability, and exposure.
One of the factors considered was the influence of antecedent soil moisture and river storages on hydrological modeling. Hurricane Helene, which made landfall in early September, saturated the soil and filled river systems to near capacity. These conditions significantly altered the hydrological response to Hurricane Milton, which struck just two weeks later. Hydrological models had to account for the already saturated soils and high river levels, which exacerbated flooding and runoff, leading locally to more extensive inundation than would have been predicted for Hurricane Milton in isolation.
Another point of focus is the impact on vulnerability, particularly the presence of debris from Hurricane Helene affecting the region's resilience. Debris obstructed drainage systems, increased the potential for secondary flooding, and complicated emergency response efforts. Additionally, the weakened infrastructure and partially damaged buildings from the first hurricane heightened the susceptibility of the population to the subsequent event, resulting in higher overall damage and more prolonged recovery periods.
Finally, the study examines the effect on exposure, including the "build-back-better" phenomenon observed in even previously to the aftermath of Hurricane Helene. While some structures were rebuilt to higher standards, providing increased resilience against Hurricane Milton, many areas remained in a state of recovery, with temporary shelters and makeshift repairs that were less able to withstand the impact of the second hurricane. This mixed state of exposure created a complex landscape for risk assessment and emergency planning.
Overall, the lessons learnt from Hurricanes Helene and Milton underscore the importance of incorporating antecedent conditions into hydrological models, considering the cumulative impacts on vulnerability, and recognizing the dynamic nature of exposure in multihazard scenarios. These insights are crucial for improving predictive models and enhancing resilience strategies in regions prone to sequential natural disasters.
How to cite: Salinas Illarena, J. L., Khoury, S., Williams, J., and Hartley, S.: Compound, Cascading, and Multihazard Perils: Lessons Learnt from Hurricanes Helene and Milton, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19347, https://doi.org/10.5194/egusphere-egu25-19347, 2025.
Severe convective storms frequently occur in Taiwan, bringing heavy rainfall, strong winds, and lightning. These events significantly disrupt critical infrastructure, including railways, by causing operational delays and damage to facilities. The proximity of the railway network to high-frequency thunderstorm zones highlights the need for tailored meteorological applications to mitigate these risks.
Heavy rainfall and wind gust are key characteristics of severe convective storms. Analysis of a thunderstorm event in Taipei Basin demonstrates that merged convective cells can produce extreme rain rates exceeding 60 mm in 20 minutes, which is closely tied to urban flash flood occurrences. Microbursts, identified through radar signatures like descending precipitation cores and strong near-ground divergent outflows, further exacerbate railway hazards, generating wind gusts exceeding 10 m/s.
To address these challenges, the Central Weather Administration issues real-time severe thunderstorm warnings based on radar observations, such as radar echoes > 55 dBZ and 60-minute rainfall > 40 mm. Since 2024, National Taiwan University has collaborated with Taiwan Railway Company to implement targeted warnings. These alerts, distributed via the LINE app, provide real-time updates on affected railway sections, improving disaster preparedness and operational resilience.
Between April and October 2024, alerts were issued for various disasters, including flooding, fallen trees, and landslides. However, the actual occurrence rate was only 2%. To reduce false alarms and enhance the accuracy of warnings, radar-based quantitative precipitation forecast (QPF) thresholds are being introduced. These efforts aim to strengthen railway safety and minimize disruptions caused by severe weather events.
How to cite: Jung, C.-J., Jou, B. J.-D., Boaz, K. P. T., Lee, Y.-H., and Yang, K.-S.: Thunderstorm in Taiwan and Its Impact on Railway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9751, https://doi.org/10.5194/egusphere-egu25-9751, 2025.
Tropical cyclones, also known as typhoons in the western North Pacific, are one of the most devastating natural disasters in the world, especially when they strike highly urbanized regions with large populations. For instance, in September 2024, two typhoons, Bebinca and Pulasan, directly affected Shanghai within 4 days, resulting in severe floods, widespread power outages, and the evacuation of more than 500,000 residents. However, there is limited knowledge about the variability and mechanism of typhoon activities in this region under the effect of climate change and urbanization. In light of these facts, we use the Weather Research and Forecasting (WRF) convection-permitting model to simulate five typhoon events that made landfall along the southeastern coast of China and severely impacted Shanghai between 2018 and 2022. By comparing with various scenarios, including the current and projected expansion of Shanghai's urban area and the 1, 2, and 3 °C rise in sea surface temperature (SST), the effects of urbanization and climate change are estimated. The results find that typhoon tracks are significantly shifted southerly away from the city by higher SST, but the typhoon risk continues to increase due to substantial enhancement of rainfall intensity and wind velocity. Warmer SST increases air temperature and decreases sea level pressure, thereby facilitating the formation and development of typhoon sizes and their dynamic systems. The southward shift of the typhoon tracks is linked to the Fujiwhara effect when two typhoons exist and interact, causing an intensified mutual counterclockwise rotation with SST increase. Urbanization further intensifies the local rainfall intensity within Shanghai due to the increase in urban surface roughness. In the future, the risk of typhoons under the compound effects of urbanization and climate warming in Shanghai and other megacities in typhoon-affected regions should be raised to attention.
How to cite: Zhuang, Q., Koukoula, M., Liu, S., Zhou, Z., and Peleg, N.: Increasing typhoon risks in Shanghai under the effect of urbanization and sea surface temperature warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1794, https://doi.org/10.5194/egusphere-egu25-1794, 2025.
Landslides, predominantly triggered by intense and prolonged rainfall, pose a critical hazard in the Himalayan region, with Indian Himalayas contributing approximately 15% of global rainfall-triggered landslides. Despite advances in landslide prediction, existing thresholds often fail to account for the diverse climatic and geophysical conditions across the Himalayas. To address these gaps, this study establishes both at-site and regional rainfall thresholds for landslide prediction by integrating advanced statistical techniques and environmental analyses. Seasonal rainfall thresholds were established to define rainy days, revealing higher winter thresholds in the Northwestern Himalayas (NWH) due to snowmelt contributions and elevated monsoon thresholds in the Northeastern Himalayas (NEH), driven by prolonged rainfall and antecedent moisture saturation. Building on this, we derived empirical event-duration (E-D) thresholds using a novel non-crossing quantile regression approach to ensure robustness against lower quantile crossing issues. The derived regional thresholds for NEH (E = -11.10 + 0.62D) and NWH (E = -12.00 + 0.63D) fits within global bounds . Land use/land cover (LULC) analysis and probabilistic mutual information ─ based analysis further identified critical environmental controls shaping these thresholds. In the NWH, built-up areas, elevation, and vegetation emerged as key factors playing significant roles in shaping rainfall thresholds to trigger landslides, while elevation, rangeland, and the Standardized Precipitation Index (SPI) were significant in the NEH. These insights underscore the need for region-specific E-D thresholds for landslide prediction and disaster management in the Himalayan region. By integrating environmental controls into a 'physics-based statistical learning' framework, this study overcomes limitations of conventional empirical rainfall threshold for landslide prediction models, delivering region-specific thresholds, thereby enhancing disaster preparedness, a step towards developing a climate-resilient landslide early warning system in the Himalayas.
How to cite: Monga, D. and Ganguli, P.: Developing Site-Specific Rainfall Thresholds for Landslide Prediction in the Himalayas: A Comparative Assessment between Northwestern and Northeastern Himalayas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7120, https://doi.org/10.5194/egusphere-egu25-7120, 2025.
Land subsidence (LS) or the relative lowering of the Earth’s ground surface is a critical concern that warrants global attention. LS is a chronic hazard in many areas that has adverse effects on built infrastructure, people, and natural systems. As global atmospheric temperatures rise and the water cycle intensifies, climatic extreme events (e.g., droughts, wildfires, heatwaves, floods) are expected to become more severe. We must therefore better understand the impact of interactions and feedbacks among extreme events, LS, human activities, and their effects around the world. Notably, our global study highlights that LS can alter the potential impacts of extreme events, and extreme events can contribute to LS. We also identify a variety of LS drivers, both natural and anthropogenic (e.g., natural compaction, urbanization, extraction of fossil fuels and groundwater from the subsurface), and corresponding LS rates throughout a variety of climatic zones and environments from the coastline inland. This study presents analysis of anthropogenic-related activities and natural processes that cause LS, but can also enhance climate change as greenhouse gases are released from the soil into the atmosphere (e.g., via permafrost thawing or peatland and wetland removal). Through our synthesis of process-driven relationships and examples, we underscore the interplay of climatic extremes and LS that damages infrastructure and enhances the vulnerability of large populations to floods and other natural hazards. Our study provides guidance for future policies and adaptation and mitigation approaches that account for the critical connections between the land surface, environmental change, and extreme events.
How to cite: Huning, L., Love, C., Anjileli, H., Vahedifard, F., Zhao, Y., Chaffe, P., Cooper, K., Alborzi, A., Pleitez, E., Martinez, A., Ashraf, S., Mallakpour, I., Moftakhari, H., and AghaKouchak, A.: Global Insight into Extreme Events and Land Subsidence: Understanding Drivers, Interplay, and Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14504, https://doi.org/10.5194/egusphere-egu25-14504, 2025.
Sardinia Island, situated in the Mediterranean Sea, is a water-scarce region frequently affected by severe multi-year droughts. This study investigates the dynamics of two distinct reservoir systems on the island—Bau Pressiu, a single reservoir with a small basin and limited storage capacity, and Flumendosa, a complex system of four interconnected reservoirs. By analyzing their monthly reservoir storage dynamics alongside the basin’s average monthly precipitation, we aim to understand their response to drought and its propagation. We employed the n-month Standardized Precipitation Index (SPI) and 1-month Standardized Storage Dynamics Index (SSDI), calculated using non-parametric fitting methods, to characterize precipitation and storage variability. Correlation analyses using Pearson and Kendall’s tau identified the precipitation accumulation period (propagation time) strongly correlated with storage dynamics. Contrasting operational rules and societal demands led to markedly different responses during droughts between the two systems. Continuous Wavelet Transform (CWT) and Cross Wavelet Transform (XWT) analyses revealed multiscale correlations between precipitation and reservoir storage. While precipitation exhibited independent multiscale power, reservoir signals displayed consistent annual-scale power linked to societal demand during summers and broader-scale patterns during severe droughts. Additionally, cross-wavelet analyses between SPI and large-scale climatic indicators, such as the Niño 3.4 index and Atlantic Multidecadal Oscillation (AMO), highlighted their significant but contrasting influences during multiyear droughts. Our findings confirm that both systems effectively mitigate short-term drought impacts. However, multiyear droughts, driven predominantly by large-scale climatic oscillations, severely strain reservoir systems and societal resilience, underscoring the so-called "reservoir effects". These insights are critical for improving water resource management strategies in drought-prone regions like Sardinia.
Keywords: multiyear drought, storage dynamics, wavelet analysis, climatic drivers, reservoir effect
How to cite: Majhi, A., Deidda, R., and Viola, F.: Unveiling the Climatic Drivers of Multi-Year Droughts in Sardinia: A Study of Reservoir Storage and Precipitation Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4161, https://doi.org/10.5194/egusphere-egu25-4161, 2025.
Building resilience in urban-rural areas against hydro-meteorological hazards such as prolonged droughts and floods is crucial for economic development and safeguarding vulnerable people in Africa. Extreme hydro-meteorological events are projected to become more frequent and intense under climate change, leading to human, material, economic and environmental losses and impacts. In particular, southern Africa exhibits pronounced hydro-meteorological extreme events in response to El Niño and La Niña events, with El Niño Southern Oscillation (ENSO) impacts projected to intensify in southern Africa in a warmer world. Two of South Africa’s major river systems have been identified as hot spots of water-related hazards, in the context of major risks of water insecurity and flood disasters in a warmer world.
The Integrated Vaal River System (IVRS), a large, complex water system comprising water resources of different river basins, and several mega-dams within, serves as a water lifeline of the Gauteng Province, the economic hub in South Africa. The IVRS is vulnerable to the occurrence of multi-year droughts. Although a drought so severe that the IVRS can no longer supply the Gauteng Province with water (a ‘day-zero drought’) has never occurred before in the historical record, a four-year drought culminating in the El Niño drought of 2015/2016 resulted in the level of the Vaal Dam falling to about 25% (a dam level below 20% would have implied the presence of a day-zero drought). East of the Lesotho highlands, major rivers such as the Umgeni drain eastwards towards the KwaZulu-Natal coastal plain. These rivers are prone to flooding, especially during La Niña years. In April 2022, South Africa experienced its worst flood disaster when more than 544 people died during flash flooding in the Umgeni, Mlazi and Mbokodweni rivers in the greater Durban area. Present analysis focuses on changes in trends and characteristics of drought and extreme precipitation events in both study regions for the past 40-years using the ERA5-Land reanalysis and observational datasets such as CHIRPS. The ERA5-Land dataset has a spatial resolution of 0.1°x0.1° (~11 km) and goes back to 1950, making it possible to analyse long-term trends of meteorological drought and extreme precipitation. Results will highlight changes in frequency, duration and intensity of hydro-meteorological extreme events.
The research is part of the “Water security in Africa – WASA” programme, project WaRisCo, which deals with water risks and resilience in urban-rural areas in southern Africa and the co-production of hydro-climate services for an adaptive and sustainable disaster risk management.
How to cite: Weber, T., Biskop, S., Schreiter, F., Ismail, M. F., Lohr, H., Schaudt, D., Fürst, C., and Engelbrecht, F.: Identifying trends in extreme hydro-meteorological events to assess water-related hazards in urban-rural areas in South Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5145, https://doi.org/10.5194/egusphere-egu25-5145, 2025.
Flood risk management comprises risk assessment through robust modeling and mitigation through measure implementation. Decision-making on mitigation measures is complicated by the plethora of criteria, stakeholder influence, implementation scale and financial constraints. Multi-criteria decision-making (MCDM) methods have emerged as valuable tools in this context, allowing for the systematic integration of diverse factors and perspectives. Nonetheless, MCDM applications in mitigation measure ranking remain challenged by the lack of informed evaluation of criteria and the diversity of measures at local reach-scale. This work aims to develop a comprehensive methodology for prioritizing flood mitigation measures. An application is conducted on a Mediterranean catchment, the Ostouane River (144 km2), Northern Lebanon. The approach involves identifying 11 intervention reaches, proposing 38 mitigation measures, and evaluating a set of 7 primary criteria decomposed into 19 multidimensional secondary criteria. We introduce criteria of effectiveness, technical, exposure and vulnerability in addition to the commonly used criteria of environmental impact, socio-economic impact, and cost. The criteria are evaluated based on qualitative and quantitative inputs derived from the literature, surveys, questionnaires, hydrological and hydraulic modelling. The TOPSIS model is employed using 6 subjective stakeholder-driven weighting methods and 6 data-driven objective weighting methods. The methodology is evaluated through a sensitivity analysis that emphasizes on the importance of measure effectiveness, environmental impact, and cost criteria in the model. Results show that subjective weighting methods tend to prioritize structural measures at downstream areas with high-value assets, while objective methods show a more balanced distribution of measures, including green solutions and upstream reaches. The total cost of the 10 prioritized measures using subjective methods is 20% higher than that of objective methods. However, the specific choice of a weighting method can imply a substantial variation in total implementation and maintenance cost. Essentially, the choice of weighting method in MCDM can significantly alter the resulting strategies and management of risk. This contrast highlights the need for policymakers to develop flexible, adaptive strategies that balance immediate protection needs with long-term sustainability goals. Overall, this work provides a novel approach for integrated flood risk management based on adapted local-scale and informed decision-making.
How to cite: Sabeh, H., Abdallah, C., Chahinian, N., Tournoud, M.-G., Hdeib, R., and Moussa, R.: Subjective and Objective Methods in Multi-Criteria Decision-Making (MCDM) for Flood Mitigation: Implications on Policymaking, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6994, https://doi.org/10.5194/egusphere-egu25-6994, 2025.
A nearly stationary low-pressure system generated significant rainfall across central Europe in September 2024 resulting in life-threatening and costly flooding in Central and Eastern Europe. Catastrophic floods also struck southern Spain in October and southern Germany from late May to early June. These events marked an escalation in severity compared to 2023, which saw major flood events impacting Italy and Greece in June and September, respectively. This escalating pattern of widespread, severe flooding, coupled with rising financial losses and risks, has drawn significant attention from (re)insurers.
We present a retrospective on these events using the Gallagher Re Europe Flood Model, a pan-European flood catastrophe model designed to assess the potential financial impact of floods in terms of their magnitude and likelihood. By using quantitative indexes to compare observed flooding with thousands of stochastic event footprints included in the model, we show that a complementary qualitative analysis is necessary to identify the most representative events. This hazard-based analysis is than complemented by the estimation of financial losses. The results reveal a range of losses for near-similar events, reflecting the complexities involved in modelling the financial impact of flooding. These complexities include, but are not limited to, the granularity of the peril, the geo-localization of the exposure and the impact of flood defences. For example, by leveraging the flexibility of our model, we show an estimate of the financial implications for a (re)insurer should the defences have failed during the development of major events.
In conclusion, while there is no control on the meteorological drivers of such events, our analyses shows the relevance and importance of catastrophe models to support (re)insurers in targeted exposure management and improved risk assessment.
How to cite: Zuccarello, F., Masafu, C., Kerschner, B., Kulkarni, S., and Taylor, L.: Recent European floods from a (re)insurance market perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9425, https://doi.org/10.5194/egusphere-egu25-9425, 2025.
Europe has been experiencing catastrophic floods. On October 19, 2024, the city of Bologna located in the Emilia-Romagna region, in central-northern Italy received 180 mm of rainfall – its average for September and October – within just 24 hours, with an intensity typical of summer thunderstorms. The region has yet barely recovered from severe flooding and landslides caused by the Storm Boris in September 18-19, 2024. These recent events followed the worst Emilia-Romagna's flood in a century, in May 2023, which resulted in 17 deaths and an estimated 8.5 billion euro in damages cost. With severe storms and their accompanying devastating floods projected to become more frequent and intense, and with an increasing concentration of people living close to rivers, Europe must urgently scale up its adaptation efforts. Understanding the preparedness of flood-prone regions and their populations is therefore crucial.
A recent survey among 1,795 residents of Emilia-Romagna conducted in July 2024 (after the devastating flood events in May 2023) investigated their flood risk awareness and preparedness to face such crises. The survey reveals that most respondents were unprepared for flood event and that providing accessible information on local flood risk can play a vital role in bolstering personal adaptation measures. Respondents reported that providing educational resources on flood preparedness and the provision of guidance on flood prevention and management are also fundamental to effective flood responses and enhanced citizens’ resilience. Effective risk communication can also generate a spillover effect, fostering broader climate awareness and a commitment to mitigation. We therefore envisage that adaptation initiatives must prioritize citizen involvement and access to reliable flood risk information. Engaging citizens as active participants in adaptation planning ensures that strategies align with local needs and are more likely to gain public support. In this way Europe can create more resilient communities and stimulate meaningful climate action.
How to cite: Ceola, S., Palazzoli, I., Binelli, C., Puglisi, C., and Muttarak, R.: Living with floods: strengthening adaptation and preparedness through better risk communication, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16503, https://doi.org/10.5194/egusphere-egu25-16503, 2025.
This study uses catchment-level statistical characterization of reanalysis and precipitation datasets to create a typology of the evolution atmospheric conditions associated with hydrologic dam incidents in the eastern United States. Extreme precipitation elevates the risk of dam overtopping, which is the main cause of a third of US dam failures. As the intensity of precipitation is predicted to increase in future climates, understanding the evolution of precipitation-generating features within the atmospheric system, alongside the hydrologic conditions leading up to the failure, is a crucial initial step in properly characterizing and predicting the risk of dam failures during a range of weather events.
This analysis divides the US eastern seaboard into four regions to examine the meteorological events within a 30-day period prior to a dam’s hydrologic incident. Initial analysis of the northeast sub-region found that although quasi-stationary fronts (frontal) or tropical cyclones (TC) present their own risk, compound events combining the two were most immediately associated with numerous dam failures over a broad region. However, catchment-level precipitation analysis further highlighted that the basins that had failures during these TC/frontal events also had numerous smaller precipitation events in the timeframe leading up to the incident. This longer tendency towards higher precipitation is associated with persistent large-scale patterns within the 14 days prior to the event. Ongoing analysis of the other sub-regions within the study area will further characterize variations across the region, as well as provide deeper insight into processes that determine how precipitation is distributed within the catchment.
How to cite: Hence, D. and Orok, H.: Characterizing the Atmospheric Conditions Leading to Dam Overtopping in the Eastern United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18032, https://doi.org/10.5194/egusphere-egu25-18032, 2025.
Coastal cities, due to their geographic location, are particularly exposed to hydro-meteorological and climate-related natural hazards. The EU-funded Horizon 2020 project SCORE (Smart Control of the Climate Resilience in European Coastal Cities), within its various activities, aims to provide a better understanding of how to mitigate and manage the effects of extreme events, particularly floods, in European coastal cities. Achieving this objective requires adequate knowledge about the probabilities and potential consequences of flood events based on a probabilistic risk assessment framework encompassing models of flood hazard for different climate scenarios, exposed elements, and vulnerability.
In this context, the present work describes the methodology and presents the results of quantitative risk assessments developed for fluvial and coastal flooding for three of SCORE’s coastal city living labs (CCLLs): Massa (Italy), Oarsoaldea (Spain) and Vilanova i la Geltrú (Spain). The risk assessments cover four types of exposed elements, i.e., population, buildings, roads, and railways, and a number of flood scenarios, both in terms of different climate conditions and considering the absence or presence of ecosystem-based approaches (EBAs) for the mitigation of fluvial flood hazard. This allows understanding both the impact that climate change is expected to have on flood risk in these CCLLs, and the influence that specific EBAs can have in reducing fluvial flood risk from a baseline to an improved infrastructural condition (i.e., residual risk).
The results of the assessments provide invaluable information to support flood risk management activities, such as gridded maps of losses for each hazard scenario and type of exposed element, maps of estimated average annual losses (AAL), and aggregate loss metrics at urban scale. In addition, they serve as input for subsequent tasks of the SCORE project, such as the development of cost-benefit analyses of specific EBA solutions and the development of financial resilience strategies for the flood risk management of the three CCLLs.
How to cite: Figueiredo, R., Rangel-Parra, R., Bussi, G., Ceresa, P., Mocali, R., Bendoni, M., Brandini, C., Campos Rodrigues, L., Riera-Spiegelhalder, M., Iglesias, J., Etxebarria, J., and Soloaga, S.: Quantitative fluvial and coastal flood risk assessments for European coastal cities considering various climate scenarios and ecosystem-based approaches for hazard mitigation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19638, https://doi.org/10.5194/egusphere-egu25-19638, 2025.
The floods that hit wide parts of Central Europe in July 2021 demonstrate the impact that extreme precipitation events can have on our continent. Heavy continuous rainfall from 12th to 15th of July 2021, caused by low-pressure system "Bernd", resulted in widespread flooding. In Germany, the federal states of Rhineland-Palatinate and North Rhine-Westphalia were particularly affected, experiencing the most fatalities and material damage. The rapid surge of rivers and creeks in these areas overwhelmed residents and authorities. After the flood, criticisms arose over inadequate crisis management and early warning systems. This raises the question of the extent to which the population was prepared for such an event and what lessons were learned to be better prepared for future climate-related hazards.
This research focuses on the question of how the experience of a highly disruptive disaster, such as the 2021 floods, affects the population's risk perception towards multiple natural hazards. Further, it assesses if severe affectedness and experiences with natural hazards trigger better preparedness and behavioural knowledge. To answer these questions, an online survey (n= >282) assesses risk perception and preparedness towards natural hazards. The survey was spread in Opladen and Schlebusch, two districts of the city of Leverkusen that were affected by the 2021 flood. Data from the survey underwent statistical analysis, including Pearson Correlation and linear regression.
Early results show that risk perception is highest for heavy rainfall, followed by river floods in both districts. However, the perception of heatwaves and drought differs in the two study areas. In Opladen, where the Urban Heat Island (UHI) effect is more pronounced, the risk of heat and drought is perceived more strongly compared to Schlebusch. We also analysed how the 2021 flood affected people's perception of natural hazard risk. Results reveal that more than 75% of respondents in Opladen and more than 60% of respondents in Schlebusch reported an altered risk perception after the 2021 floods. Before this event, the risk perception towards extreme precipitation and river flooding was notably lower. Of all natural hazards mentioned in the questionnaire, heat was perceived as the greatest threat in Opladen, while in Schlebusch it was storms.
The findings of this study will be used in the BMBF project Co-Site to design risk communication strategies and workshops aimed at enhancing the public’s preparedness for natural hazards. Understanding people’s risk perception and preparedness for natural hazards can help identify training needs for better preparedness and foster appropriate communication about disaster risk.
Keywords: Risk Perception, Natural Hazards, Preparedness, Germany
How to cite: Könsgen, I., Braun, B., and Nehren, U.: How do disruptive events influence risk perception and preparedness towards natural hazards? An empirical study in Leverkusen, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8371, https://doi.org/10.5194/egusphere-egu25-8371, 2025.
Maharashtra is India’s second-largest state in population and third-largest in area. It faces escalating environmental challenges from diverse hydroclimatic extremes, including droughts, floods, and cyclones. IPCC reports underscore the need for a comprehensive understanding of socioeconomic vulnerability (SEV) to address the inequality and differential impacts of these hazards within a robust risk assessment framework. Several national and regional vulnerability assessments have been conducted in India and Maharashtra. These studies lack a finer-resolution assessment of socioeconomic vulnerability (SEV), limiting the understanding of localised variations. They also fall short of incorporating a broad range of SEV indicators, which hinders comprehensive vulnerability analysis. The major drivers contributing to vulnerability need to be identified.
The current study advances local adaptation planning by thoroughly evaluating socioeconomic vulnerability (SEV) at Maharashtra's finest resolution of sub-district (talukas/tehsils) level based on the availability of the demographic data. The study utilised composite indicators, which were procured and derived from the latest available Census of India (CoI, 2011) data. This method offers a thorough grasp of susceptibility patterns by concentrating on the finest possible spatial resolution based on the limited availability of the resource for socioeconomic indicator information. The subjectivity constraints of weighing these socioeconomic indicators have been addressed using the non-parametric Data Envelopment Analysis (DEA) optimisation technique. The study also utilised variance-based factor analysis to identify the major contributing drivers of the SEV for Maharashtra. Additionally, a localised cluster-level SEV analysis is also performed based on multiple administrative divisions to identify the local-level significant indicators. Applying this methodology to 357 sub-districts of Maharashtra reveals a concentration of highly vulnerable sub-districts in the Central and Eastern Vidarbha Zone, moderately vulnerable districts in the Central Maharashtra Plateau Zone, and less vulnerable districts in the North Konkan Coastal. The factor analysis results also highlight agricultural labourers, marginal working populations, and marginal female working populations as the most critical drivers influencing vulnerability for the entire Maharashtra State.
This proposed framework is generic and comprehensive and can be applied to any other state or spatial scale. The results of this study can assist policymakers and stakeholders in identifying vulnerable hotspots and developing proper social and economic policies to better understand and improve the socioeconomic situations of Maharashtra at the sub-district scale.
Keywords: Data envelopment analysis, Principal component analysis, Socioeconomic indicators, Sub-district level, Vulnerability analysis.
How to cite: Dev, I., Chakraborty, A., and Karmakar, S.: A Comprehensive Socioeconomic Vulnerability Analysis Using Robust DEA Technique at the Finest Resolution of Sub-District Scale in Entire Maharashtra State of India: Identifying Significant Vulnerability Drivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14777, https://doi.org/10.5194/egusphere-egu25-14777, 2025.
Climate change intensifies the global hydrological cycle, altering hydrometeorological variables and amplifying flood risks, with significant social, economic, and environmental consequences. Reliable flood estimates are crucial for designing cost-effective flood protection structures. The assessment often focusses only on peak discharge, overlooking vital factors like flood wave frequency, duration, and time to peak, which are key elements for preparedness and resilience. Although. the use of general circulation models (GCMs) for future simulations has advanced our understanding of catastrophic floods under climate change. Yet, the socio-economic impacts of these events remain insufficiently explored, leaving crucial vulnerabilities inadequately addressed. This study therefore evaluates the flood characteristics and socio-economic vulnerabilities in a large river basin using downscaled GCMs of CMIP6. The hydrological and hydrodynamic models were used for determining the flood wave characteristics considering non stationarity. We also examine the benefits of limiting global warming to 1.5°C, aligned with COP28 goals, by assessing global warming levels of 1.5°C, 2°C, and 3°C and the EF (2021–2050) and FF (2071–2100).
The flood peaks in major cities are projected to rise by 10–14% during pre-monsoon and monsoon seasons, with high-warming scenarios causing a ~35% increase in high flow by 2100. However, limiting the warming to 1.5°C could reduce the return flood discharge by 9,000 m³/s in FF. The projections indicate a paradigm shift in the flood wave characteristics of the basin, with a notable increase in both flood wave duration (~0.31 days per year) and frequency (~3 more flood waves) during the pre-monsoon and monsoon seasons. Socio-economic vulnerability assessments reveal heightened risks under high-warming scenarios, driven by population growth and intensified hydroclimatic extremes, leading to greater inundation extents, depths, and displacement risks. These findings underscore the urgent need for global and regional cooperation, evidence-based policies, and climate-resilient infrastructure to mitigate flood risks and adapt to evolving hydroclimatic extremes in vulnerable transboundary basins.
How to cite: Gupta, R. and Chembolu, V.: Flood Vulnerability under High-Warming Scenarios: Insights from flood wave Projections and Socio-Economic Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1079, https://doi.org/10.5194/egusphere-egu25-1079, 2025.
Severe short-duration thunderstorms are a characteristic part of summer rainfall in the southeastern Alpine forelands. These heavy convective precipitation events (HCPEs) pose a severe risk to the region in the form of flash floods and landslides. Despite their crucial role in summer rainfall and natural hazards, the moisture sources and spatial structure of such HCPEs are still largely unknown.
The presented study links these highly localized events to large-scale processes to identify possible moisture source regions through backward trajectories obtained from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model runs with ERA5 data. To complement this large-scale analysis, we use high-resolution data from the dense WegenerNet climate station network in southeastern Austria, to investigate the local characteristics and spatial structure of HCPEs.
The combination of large- and local-scale analysis results in a multi-faceted picture of HCPEs and their characteristics. We find that temperature is a key driver of HCPEs and that moisture from the Mediterranean region is a key influencing factor on the occurrence, magnitude, and spatial extent of such events in the study region. Furthermore, we find differences in the storm characteristics depending on the season and region of moisture source.
From a more general perspective, our findings imply that rises in temperature and humidity will likely result in more intense HCPEs with larger spatial extents, which potentially will increase the severity of floods and other natural hazards and hence also the damage risks in the southeastern Alpine forelands.
How to cite: Haas, S., Peleg, N., Kirchengast, G., and Fuchsberger, J.: Sources and characteristics of short-duration heavy convective precipitation events in the southeastern Alpine forelands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1255, https://doi.org/10.5194/egusphere-egu25-1255, 2025.
The Tienshan Mountains of Central Asia, a key region in global arid and semi-arid zones, faces highly uneven precipitation distribution due to its unique topography and climate. Precipitation variations significantly affect the region’s ecosystems, agriculture, and hydrological security. While extreme heavy precipitation has been widely studied, research on extreme light precipitation is limited. Additionally, spatial distribution patterns and driving mechanisms of extreme events under varying climatic and geomorphic conditions remain underexplored. This study systematically examines the spatial-temporal trends of extreme hydro-climatic events in the Tienshan Mountains, focusing on both heavy and light precipitation, to provide insights for water resource management and disaster prevention.
The Tienshan Mountains have experienced significant changes in extreme hydro-climatic events since 2000. The frequency anomaly of extreme light precipitation events (R1p) shifted from positive to negative, indicating a marked decline compared to the historical average, while extreme heavy precipitation events (R99p) shifted from negative to positive, reflecting a substantial increase in frequency. The intensity of both events has also risen notably during this period. Spatially, the intensity variations of extreme events show consistent signals across the Tienshan region, while frequency exhibits strong spatial heterogeneity. Around 80°E, extreme heavy precipitation frequency increases eastward and decreases westward. Vertically, mid-altitudes exhibit the most pronounced changes. The frequency of extreme light precipitation declines at 0.471 days/year in mid-altitudes compared to 0.356 days/year at high altitudes. Similarly, extreme heavy precipitation intensity increases at 0.106 mm/year in mid-altitudes, much higher than 0.014 mm/year at high altitudes. These patterns result from the combined effects of Tibetan Plateau thermal dynamics and monsoon-driven moisture transport, creating distinct differences in extreme precipitation between the eastern and western Tienshan. Future studies should explore the interactions between the plateau and atmospheric circulation to improve the prediction and mitigation of extreme events, aiding water resource management and disaster preparedness.
How to cite: Zhang, X., Chen, Y., Li, Z., Sun, F., Li, Y., and Hou, Y.: Regional Disparities in Hydro-climatic Extremes Across Central Asia: Insights from the Tienshan Mountains, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1636, https://doi.org/10.5194/egusphere-egu25-1636, 2025.
Serbia lies between Central and Southern Europe and is characterised by a complex topography, with the Pannonian Plain in the north and the Dinaric Alps in the west and southwest. Three climate types characterise Serbia: continental climate in the north, temperate continental climate in the central part and modified Mediterranean climate in the south. Precipitation in Serbia is generally the result of passing cyclones and associated atmospheric fronts as part of the general circulation of the atmosphere in the mid-latitudes (Tošić et al., 2017). In recent decades, flash flooding resulting from extreme precipitation events has proven to be a great threat to human life and a great cause of economic strife (an estimate of 1.7 billion euros in damages in 2014 alone when catastrophic flooding occurred in Bosnia, Croatia and Serbia).
The highest yearly 1-day precipitation (Rx1day) was analyzed on an annual and seasonal basis at ten stations in Serbia in the period 1961-2020. The modified Mann-Kendall test was used to examine the significance of the trend. An increase was observed in all annual time series of Rx1day. A significant positive trend was observed at 9 out of 10 stations. The Rx1day time series increased in Niš in southern Serbia, but not significantly. In addition, all fall and spring time series showed a positive trend, of which 8 and 5, respectively, were significant. In summer, 5 stations (Zrenjanin, Novi Sad, Veliko Gradište, Kragujevac and Zaječar) showed a significant positive trend, while 4 stations (Sremska Mitrovica, Belgrade, Loznica and Kragujevac) showed a positive trend and one (Niš) showed a negative but non-significant trend. In winter, a significant increase in Rx1day was observed at two stations (Kragujevac and Zaječar) and a negative trend at Veliko Gradište. The generalised extreme value function was calculated and analyzed for all of the available stations, for the periods of 1961-1990, 1990-2020 and 1961-2020 with the inclusion of return periods.
The highest increase of Rx1day was observed in Novi Sad, both on an annual and seasonal basis. The highest summer value of Rx1day (116.6 mm) was measured in Novi Sad in 2018, which led to flooding in the city (Savić et al., 2020). This precipitation episode was determined to be caused by convective rainfall.
Tošić, I., Unkašević, M., Putniković, S., 2017: Extreme daily precipitation: the case of Serbia in 2014. Theor. Appl. Climatol. 128, 785–794. doi:10.1007/s00704-016-1749-2
Savić, S.; Kalfayan, M.; Dolinaj, D. Precipitation Spatial Patterns in Cities with Different Urbanisation Types: Case Study of Novi Sad (Serbia) as a Medium-sized City. Geogr. Pannon. 2020, 24 (2), 88–99. https://doi.org/10.5937/gp24-25202
How to cite: Filipovic, L., Tosic, I., de Silva, A. S. A., Stosic, B., Stosic, T., and Djudjevic, V.: Analysis of extreme precipitation timeseries in Serbia based on station data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1877, https://doi.org/10.5194/egusphere-egu25-1877, 2025.
Pluvial floods are and will remain an important driver of flood risk, especially in an urban context. Recently, several floods triggered by extreme rainfall made the news and led to many casualties, such as those in Valencia and Nepal in 2024. To better prepare for such disasters, urban planners may use pluvial flood maps to assess flood risk and plan accordingly. Typically, such maps are produced by distributing rainfall over topography using a hydraulic model which solves some variation of the shallow water equations. While the decision for a specific hydraulic model may impact pluvial flood maps, here we will focus on the role of pluvial input data.
Typically, intensity-duration-frequency (IDF) data is used to drive these models, yet these data are highly uncertain due to, for instance, the absence of accurate rainfall observations or the application of extreme value statistics.
Here, we present results of a sensitivity analysis in which we employed a range of global and national IDF data sets, such as NOAA Atlas 14, KOSTRA-DWD, BURGER, GPEX, PPDIST and PXR. Each data set is unique in the amount of data it was produced with, the spatial extent, the spatial regionalization of point-based estimates, the extreme value distribution used, and so forth. All IDF datasets were fed into a hydraulic model (LISFLOOD-FP) using the Chicago Design Storm (CDS) method to produce consistent and comparable maps of pluvial flood hazard for several test cases. Subsequently, the (dis-)agreement of the flood maps obtained is assessed.
To convert flood maps into impact, they are intersected with exposure data to obtain an estimate of average annual exposure (AAE) to pluvial floods, which is a better measure for assessing the impact of these floods.
While we expect that intensities extracted from the different IDF data sets will differ markedly, this study will shed light on the impact these differences may have on flood hazard and flood exposure estimates.
How to cite: Hoch, J., Cooper, A., and Lamb, C.: Sensitivity of pluvial flood exposure to the selection of intensity-duration-frequency data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2722, https://doi.org/10.5194/egusphere-egu25-2722, 2025.
The consuming-time of the algorithm for solving the reservoir optimal operation model is crucial to real-time flood control. The traditional DP-POA (Dynamic Programmin-Progressive Optimization Algorithm) has better solutions but takes a long time. This study proposed an improved DP-POA method, which effectively reduces the amount of calculation and improves the calculation speed by simplifying the objective function. Taking Yuecheng Reservoir in China as an example, this study conducted a comparative analysis of five algorithms, including improved DP-POA, traditional DP-POA, improved POA, traditional POA and PSO (Particle Swarm Optimization). The results show that the improved DP-POA exhibits significant advantages in both consuming-time and solution quality. In the 2021 flood case, compared with the traditional DP-POA, the consuming-time of the improved DP-POA is shortened from about half an hour to less than 5 minutes; meanwhile, the solution of the improved DP-POA is better than or basically equal to other comparative methods.
How to cite: Ning, Y., Ren, M., Zhang, J., Tang, R., Zhao, L., and Wang, G.: An Improved DP-POA Method for Optimal Operation of Reservoir Flood Control, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5363, https://doi.org/10.5194/egusphere-egu25-5363, 2025.
Floods count as some of the most devastating natural disasters, inflicting extensive damage on infrastructure, disrupting communities, and posing serious threats to human lives. The flooding in Germany’s Ahr Valley in 2021 is a strong reminder of the devastating consequences. The increasing intensity of such events, driven by climate change, underscores the urgency of enhanced prevention and preparedness strategies (Deumlich & Gericke, 2020).
Fluvial (river) floods, which often occur at regular intervals, tend to remain in the collective memory of affected populations. However, when sufficient time passes without an event, a phenomenon referred to as "flood dementia" can emerge. This leads to diminished public awareness and preparedness, increasing vulnerability during future disasters. The issue is even more pronounced with pluvial (rainfall-induced) floods, which are harder to predict and therefore require robust preventive measures.
Effective flood risk management demands targeted approaches to engage diverse demographic groups. A survey conducted as part of the BMBF-FloReST project revealed significant disparities in awareness across age groups. While individuals aged 50 and older were well-represented in the survey, those aged 20 and younger were notably underrepresented. This younger age group often lacks the life experience needed to fully comprehend the impacts of pluvial flooding, underscoring the importance of targeted educational initiatives.
StoryMaps have emerged as a valuable tool for addressing this gap, particularly among younger audiences. By integrating geospatial data visualization with storytelling elements such as maps, images, videos, and narratives, StoryMaps transform complex environmental information into an engaging and accessible format. Young people, who are more responsive to interactive and visually rich content, benefit from enhanced comprehension and retention. For example, StoryMaps can depict flood-prone areas, recount historical flood events, and simulate potential outcomes of mitigation strategies, thus bridging technical concepts with tangible, real-world examples.
Furthermore, StoryMaps help young people connect local flood risks to broader global challenges. By exploring the links between climate change and flooding, students can better understand the interconnectedness of environmental issues. This fosters a sense of accountability and encourages proactive participation in community resilience initiatives. Additionally, StoryMaps promote critical thinking by enabling users to explore “what-if” scenarios, such as the impacts of improved drainage systems or reforestation on flood dynamics.
Their digital accessibility makes StoryMaps particularly effective for engaging tech-savvy younger generations. They can be seamlessly incorporated into school curricula, workshops, and community outreach programs, equipping young people with practical knowledge about sustainable water management and disaster preparedness.
In conclusion, StoryMaps represent a forward-thinking approach to flood risk awareness and education, particularly for younger audiences. By blending education with engagement, they empower a generation to better understand and address the challenges of climate-related disasters. Our presentation will showcase two StoryMaps—focused on the 2021 Ahr Valley flood and the 2024 Saarland Pentecost flood—developed as part of the FloReST project and introduced in schools to foster awareness and resilience among young learners.
How to cite: Fischer-Stabel, P., Hoffmann, J., and Azvedo, J.: StoryMaps: Advancing Public Awareness, Preparedness, and Resilience to Flood Risks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5958, https://doi.org/10.5194/egusphere-egu25-5958, 2025.
Reunion Island, situated in the Indian Ocean, has faced significant flood risks since its early settlement in the 17th century. Currently, the island comprises six territories identified as flood-risk areas (TRI). Understanding the historical context of this risk is crucial for effective management and adaptation strategies. To explore the evolution of flood risk, we examined a collection of historical postcards from the late 19th to early 20th centuries, archived at the Archives Départementales in Saint-Denis. We selected approximately fifty postcards based on specific criteria: the relationship between habitats and rivers, the need for a comprehensive spatial perspective, and the representation of diverse watersheds across the island. Field missions conducted in 2024 and 2025 allowed us to replicate the photographs at the same locations as depicted on the ancient postcards, facilitating a direct comparison of changes in land use and hydromorphological structures (including “planèzes”, slopes, and valley floors). Our findings reveal significant insights comparing land use from the late 19th century to the present day (2024-2025). We observed new housing developments on planèzes, which have heightened risks of urban runoff and flooding associated with small rivers. Certain regions remain unchanged, indicating that the original placement of habitats was appropriate, situated on alluvial terraces and slopes protected from landslides and debris flows. In contrast, urban encroachment into the active channels of large rivers (“ravines”) has created substantial risks for local populations. These findings align with the analyses of D. Lorion (2013), who characterizes the rise in flood-risk areas during the 1970s and 1980s as a manifestation of the 'security utopia' created by river embankment systems.
References
Lorion D. (2013) – From a utopia of security to the integrated management of drainage basins: The example of Reunion Island (France). In Arnaud-Fassetta G., Masson E., Reynard E. (Eds.) European continental hydrosystems under changing water policy. Friedrich Pfeil Verlag, München, 87-98.
How to cite: Arnaud-Fassetta, G., Larive, J., Taglioni, F., Lorion, D., Dahech, S., and Méchain, A.: Adaptation to flood risk on Reunion Island (France): A historical perspective from photographic evidence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6246, https://doi.org/10.5194/egusphere-egu25-6246, 2025.
This study describes the activities developed within the project “raINfall exTremEs and their impacts: from the local to the National ScalE (INTENSE)”, funded by the Italian Ministry of University and Research (MUR) and by the EU. INTENSE will provide a novel assessment of hazards related to extreme rainfall and landslides, to aid risk management at the local and national scales.
The long historical rainfall records available from rain gauges allow us to derive extreme precipitation probabilities in gauged locations, but they hardly represent ungauged areas and cannot adequately sample the spatial variability of extreme rainfall in areas with strong climatological gradients, such as orographic and coastal regions. To overcome these limitations, we collect national-scale observations from rain gauges, weather radars and satellites and we use state-of-the-art statistical approaches, stochastic weather generators, and physically based landslide models.
In particular, a novel statistical approach for the analysis of extreme values from remotely sensed rainfall is used to produce national scale maps of extreme rainfall at multiple scales. The INTENSE approach allows us to link local rainfall climatology (i.e. frequency of rainstorms; intensity of ordinary and extreme rainstorms; rainstorms temporal structure) to the probability of initiation of shallow mass movements, a long standing challenge in rainfall-related hazards assessment. This is done feeding physically based landslide initiation models with long simulations of climate variables able to adequately represent the statistics and properties of both ordinary and extreme rainstorms.
We present here the preliminary results of the project with a particular focus on (i) rainfall frequency analysis, (ii) downscaling of extreme precipitation, and (iii) of the critical soil moisture maps needed to trigger shallow movements in a selected case study.
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
How to cite: Arnone, E., Marani, M., Noto, L. V., Paranunzio, R., Darienzo, M., Francipane, A., Sanchez Pena, C. A., Thomas, J., Treppiedi, D., and Marra, F.: Rainfall extremes and their impacts: from the local to the National Scale. The INTENSE project. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7334, https://doi.org/10.5194/egusphere-egu25-7334, 2025.
The assessment of compound flood risk often relies on the assumption that the dependence structure between flood drivers (e.g., rainfall intensity, coastal water levels, and streamflow) remains stationary under changing climatic conditions. Yet, traditional approaches that inherently assume stationary dependencies, or rely solely on historical relationships, may misrepresent flood risk and fail to identify hotspots of emerging infrastructure vulnerabilities. This study aims to (a) characterize the dependence structure between compound flood drivers using a parsimonious parametric framework, and (b) explore potential changes in this structure under future climate scenarios, by leveraging outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) regional climate projections. An ensemble of synthetic and historical storms is employed to simulate flood impacts across the Greater Boston region, forming the basis for statistically modeling the conditional dependence of the main flood drivers. Changes in the marginal distributions of these drivers, informed by CMIP6 simulations under various Representative Concentration Pathways (RCPs), are also integrated into the dependence framework to evaluate future trajectories of compound flood risk. The findings focus on determining whether shifts in the dependence structure offer a more nuanced understanding of evolving flood risk profiles, as well as identifying areas where traditional stationary assumptions may result in systematic errors. Ultimately, the study advances understanding of the dynamic interplay between flood drivers under future climate scenarios, and supports the development of adaptation strategies for regions vulnerable to compound flooding.
How to cite: Emmanouil, S., Langousis, A., Perry, E., Hacker, J. P., and Anagnostou, E. N.: Evolving Dependence Structures Between Compound Flood Drivers Under Future Climate Scenarios: A case study over Greater Boston, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7484, https://doi.org/10.5194/egusphere-egu25-7484, 2025.
Disaster monitoring and early warning systems are typically associated with the detection of extreme events capable of causing significant social impacts, particularly in cases of rain-related disasters such as floods, flash floods, and landslides. However, this traditional approach—focused solely on assessing the likelihood of threats materializing—proves insufficient when monitoring areas with high heterogeneity in terms of exposure and population vulnerability. In such cases, less extreme but more frequent events can result in recurring impacts that, when analyzed historically, surpass those of extreme events. In Brazil, approximately 90% of landslide occurrences are associated with low magnitude impact. Low magnitude events cannot be neglected because even though they cause low-severity losses, their high-frequency and cumulative effect adds up to a large number of losses and affected people. Understanding the impacts of low magnitude events can aid in defining risk scenarios as part of the potential impact dimension within a risk matrix. Thus, this study uses a database developed by the Brazilian National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) to better understand these relationships. Furthermore, it proposes an approach to develop a potential impact indicator based on retrospective risk analyses, linking average impact levels over time to extreme rainfall frequency data. The study focuses on Santa Catarina state (Southern Brazil), analyzing impact data from 80 municipalities between 2016 and 2024. During this time period, the monitored municipalities in the state reported 568 landslide/related impact events, affecting over 8,000 individuals. The analyzed data indicate 548 events with low magnitude impacts, which can be classified as extensive risk events (high frequency, low severity), typically characterized by situations that had 1 to 2 small landslides. On the other hand, 18 events were identified with medium magnitude impacts, where 3 to 10 landslides were generally recorded. Only 2 large magnitude events (>10 landslides) were recorded in the analyzed period, which can be classified as intensive risk events (low frequency, high severity). The results reveal distinct municipal profiles, highlighting two key scenarios: i) areas where the combination of frequent heavy rainfall events and a high potential impact indicator result in very high climate risk and, ii) contrasting situations where significant impact occur despite of low frequency of heavy rainfall suggesting a bigger weight of social vulnerability and exposure of human systems. In addition to providing critical insights for enhancing CEMADEN's decision-making in disaster early warning issuance, the study offers valuable information for prioritizing risk reduction measures and climate adaptation actions.
How to cite: Bernardes, T. and Camarinha, P.: Comparative analysis between impact data related to landslides and extreme rainfall events in Southern Brazil: a proposal to establish potential impact indicators, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11982, https://doi.org/10.5194/egusphere-egu25-11982, 2025.
The Brahmaputra River basin, a complex hydrological system in South Asia, is among the most flood-prone regions in the world. It frequently experiences severe and devastating flood events. The floods are closely linked to the region’s complex atmospheric moisture dynamics, which govern the spatiotemporal distribution of precipitation. However, the mechanisms driving extreme precipitation events, especially their connection to large-scale moisture transport, remain poorly understood. We investigate the role of Integrated Vapor Transport (IVT) in the initiation and intensification of extreme flood events within the Brahmaputra basin. We analyzed the spatial and temporal patterns of IVT and their correlation with changes in patterns of precipitation. Our findings indicate that IVT, characterized by strong moisture flux convergence, is closely associated with significant increases in rainfall intensity, particularly during the summer monsoon season. The improved understanding of the physical mechanisms behind precipitation intensification can significantly improve forecasting and early warning systems for extreme flood events. These advancements are crucial for mitigating the impacts of extreme floods and enhancing the actionable strategies in one of the world’s most vulnerable regions.
How to cite: Vangala, G. and Mishra, V.: Role of moisture transport in extreme flood events in the Brahmaputra basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14685, https://doi.org/10.5194/egusphere-egu25-14685, 2025.
Scenarios are commonly used in alpine hazard risk management. They can serve different purposes such as design of structures and mitigation measures, risk analysis for the prioritization of measures and the allocation of resources, and in preparing for the unexpected. In scenario-based quantitative risk analysis, few scenarios are used to obtain an estimate of risk, i.e., the annual expected losses, by approximating the loss exceedance curve. The scenarios are frequently selected from a range of plausible hazard intensities, such as discharges for hydrologic hazards or volumes for gravitational hazards and evaluated in terms of their expected consequences.
In the absence of long event records and lack of comprehensive data collection (e.g., from measurement stations or field investigations), as is often the case in alpine catchments, it can be difficult to assign occurrence probabilities to the specified hazard intensities. The recurrence of the scenarios (and thereby the expected consequences) is frequently equated with the recurrence of meteorological trigger conditions, thereby neglecting the effects of necessary preconditions for hazards to occur. In turn, to consider preconditions as additional parameters in evaluating the recurrence of expected consequences, it is required to adapt the development of the loss exceedance curve. For that purpose, we derive the unconditional probability distribution of the expected consequences from the distributions of damages conditional on the preconditions.
Using the example of an alpine catchment, we illustrate how considering preconditions invalidate the assumption of equating the recurrence frequency of the triggering conditions with the recurrence frequency of the consequences. We investigate the impact of considering different preconditions on the risk estimates by modelling the physical response of the natural environment to these trigger conditions. The information about frequency and magnitude of hazard scenarios is combined with the probability of different preconditions to derive scenarios that are representative of consequences with given recurrence frequency, hence better reflect the overall risk.
How to cite: Hoffmann, A. and Straub, D.: Investigating the impact of considering hazard preconditions in scenario-based risk estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20548, https://doi.org/10.5194/egusphere-egu25-20548, 2025.
Despite the growing efforts in quantifying disaster vulnerability, its assessment at the building scale remains a challenge. In this study, we aim to quantify the socio-economic vulnerability index (SEVI) for every building by combining its housing price data with SEVI values at sub-district level. The methodology consists of three main steps. First, the latest social and economic data from Gwangju and Jeollanam provinces of Youngsan watershed were collected at sub-district and district levels. These data served as inputs for the Principal Component Analysis (PCA) algorithm to compute SEVI at sub-districts level. Second, housing price data were gathered for as many residential buildings as possible and combined with the SEVI values of their associated sub-districts. This combination was conducted with an assumption that households with more expensive housing are less vulnerable to natural disasters. Finally, a geocoding technique was adopted to tranform physical addresses into geospatial locations, enabling the assignment of vulnerability values into building polygons for further analysis and visualization. The outcome of this study is a map detailing the vulnerability levels of individual buildings. The main findings reveal that (1) the Southeastern part of Youngsan watershed tends to be more vulnerable to disaster, with sub-districts exhibiting high SEVI levels mostly located near the Youngsan River; (2) sub-districts with the highest number of highly vulnerable buildings tend to have only medium SEVI levels. By integrating these insights into disaster risk mitigation efforts, policymakers can develop more detailed and effective strategies for both short and long term, focusing on each building individually.
Acknowledgement
This study was supported by Korea Environment Industry & Technology Institute (KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Program (or Project), funded by Korea Ministry of Environment(MOE)(RS-2023-00218873).
How to cite: Vu Quoc, H., Kim, D., and Vuong Tai, C.: Socio-economic vulnerability assessment at building scale: A case study in Youngsan watershed, South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17944, https://doi.org/10.5194/egusphere-egu25-17944, 2025.
Extreme precipitation events in the Indian sub-continent have profound socio-economic and environmental impacts, particularly due to their role in triggering flash floods. These events are driven by a combination of atmospheric conditions, moisture sources and pathways, geomorphology, and hydrometeorology. However, while the hydrometeorological and geomorphological factors have been extensively studied, the role of atmospheric drivers and moisture pathways remains underexplored, creating a significant research gap. To address this gap, we analyzed the atmospheric processes and moisture sources contributing to widespread extreme hourly precipitation events across the Indian subcontinent during the period 1981–2020. Using a combination of reanalysis datasets, event detection algorithms, and moisture tracking methods, we identified the spatial and temporal distribution of these events. We find the Himalayas as a major hotspot, with most extreme events occurring during the Indian summer monsoon season. We find recycled moisture from land surfaces is the dominant source of moisture in the Himalayas, whereas moisture from the Arabian Sea and the Bay of Bengal primarily drives precipitation extremes in peninsular India. Our findings highlight the interconnected dynamics between the atmosphere, land, and ocean in driving extreme precipitation. The study underscores the importance of incorporating atmospheric drivers into disaster management frameworks and early warning systems to enhance preparedness and mitigate impacts effectively.
How to cite: Dilip K, N. and Mishra, V.: Atmospheric drivers of extreme precipitation events in the Indian sub-continent, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10315, https://doi.org/10.5194/egusphere-egu25-10315, 2025.
Climate change has increased the risk of extreme precipitation and flooding in India. During the 2024 summer monsoon season, three major extreme precipitation events occurred across the western, southern, and northern states of India, leading to widespread flooding in these regions. We examine the causes and impacts of extreme precipitation and flood events using a combination of observational data, reanalysis datasets, and hydrological models. In all the three regions, extreme rainfall occurred immediately after multiday continuous precipitation, resulting in catastrophic flooding. The 3-day extreme precipitation that caused flooding in the three regions had return periods of more than 75 years, 100 years, and 200 years, respectively. The primary moisture source for the Gujarat floods (western India) was the Arabian Sea, while the floods in Andhra Pradesh and Telangana (southern India) were driven by dual moisture advection from both the Arabian Sea and the Bay of Bengal. For the floods in northern India, the dominant moisture sources were recycled land moisture and southwest moisture transport from the Arabian Sea. These moisture inflows, combined with favorable atmospheric conditions and pre-existing saturated soils, resulted in severe flooding across all regions. Our findings underscore the escalating challenge of managing such extreme events as their frequency and intensity rise with global warming.
How to cite: Singh Chuphal, D., Malik, I., Singh, R., Vangala, G., Naik, M. N., Vegad, U., Dilip K, N., Mukhopadhyay, P., Selvan, J. P., Kapadia, V., and Mishra, V.: Multi-day extreme precipitation caused major floods in India during summer monsoon of 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14945, https://doi.org/10.5194/egusphere-egu25-14945, 2025.
This research examines the relationships between vulnerability and resilience concerning flash flood risk in the Castilla y León region (Spain). The study compares vulnerability and resilience indices and investigates the relationships between their elements and flash flood risk variables. It discusses the necessity of enhancing vulnerability and resilience evaluations by integrating diverse aspects, encompassing social, economic, ecosystem, physical, institutional, and cultural dimensions. The methodology incorporates statistical and spatial approaches, such as Spearman correlation, bivariate choropleth maps, and regression models. The study reveals that vulnerability and resilience are related but represent distinct constructs. Despite a weak correlation between the vulnerability and resilience indices (r = 0.06), significant correlations exist among various elements within these indices. This underscores the need for a multidimensional approach that combines theoretical frameworks with practical implementation, providing insights for policy makers and guiding future research efforts. For example, the resilience index and the vulnerability index's exposure component are strongly correlated (r = 0.40). The spatial relationships are more evident between the vulnerability and resilience indices, with a local R2 of 0.74 between the resilience index and the different dimensions within the vulnerability index. The study also finds significant correlations between specific vulnerability elements and flash flood risk variables, particularly in the exposure component (r = 0.59 for the population at risk) and the institutional dimension (r = -0.48 for the total flood indemnities provided by the insurance company). Notably, the vulnerability and resilience indices show a strong spatial relationship with critical infrastructure at risk, with a local R2 of 0.85. This research highlights the need for more research to improve vulnerability and resilience assessments and tailor them to specific local contexts. This underscores the need for a multidimensional approach that combines theoretical frameworks with practical implementation, providing insights for policy makers and guiding future research efforts.
How to cite: Bodoque, J. M., Aroca, E., and García, J. A.: Comparative Analysis of Flash Flood Vulnerability and Resilience through Multidimensional Indices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18771, https://doi.org/10.5194/egusphere-egu25-18771, 2025.
Impact Forecasting, a catastrophe model development branch of Aon, develops catastrophe models for various countries and perils, including floods, windstorms, earthquakes, wildfires, hurricanes, and typhoons. These models are crucial for the insurance and reinsurance industry to estimate losses in terms of severity and frequency.
To address the increasing demand for evaluating losses across multiple countries and perils, Impact Forecasting has started using large ensembles of global climate models (GCM) and regional climate models (RCM). These models serve as a common forcing input for catastrophe models related to atmospheric perils such as flooding (fluvial, pluvial, and coastal), summer storms, windstorms, and wildfires.
The use of GCM/RCM as common forcing input offers two main advantages:
- Spatial Consistency: The data are spatially consistent at a global or continental scale, which helps in addressing the issue of cross-country correlations.
- Variability: The large number of available ensembles provides sufficient variability to build a representative stochastic catalogue of potential catastrophes.
We will present several examples of this approach (Pan-European flood model, the Canadian flood and wildfire models), where common GCM/RCM inputs are used to provide a consistent view of losses across large regions and various perils. We will also show how we adress the issue of low resolution of GCM/RCM models using machine learning.
How to cite: Kadlec, M. and Švandová, A.: Large scale atmospheric cross-peril stochastic catastrophe models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19293, https://doi.org/10.5194/egusphere-egu25-19293, 2025.
Accurate flood forecasting is essential to mitigate the impacts of extreme rainfall on communities and infrastructure. Traditional hydrological prediction methods often rely on rain gauge data and numerical models, which can be limited in capturing precipitation's spatial and temporal dynamics, particularly during intense or rapid-onset events. X-band polarimetric radar provides a valuable alternative for quantitative rainfall estimation, offering finer spatial and temporal resolution crucial for hydrological applications.
This study investigates the integration of radar nowcasting into flood forecasting workflows, focusing on data from an X-band polarimetric radar operated by the Civil Protection Service of the Marche Region, Italy. Several case studies have been analyzed considering different precipitation regimes: convective events with a short-time peak of intense rainfall and stratiform events, characterized by several hours of persistent precipitation associated with frontal systems.
The Cetemps Hydrological Model (CHyM) is used to simulate river discharge and assess hydrological stress indices under three scenarios: (1) rain gauge data alone, (2) radar data alone, and (3) radar data integrated with nowcasting outputs to generate 1-hour forecasted rainfall fields. Results demonstrate that radar-based nowcasting significantly improves flood prediction accuracy and lead time, particularly in flash flood scenarios driven by convective systems.
This study highlights the importance of radar nowcasting techniques in improving flood forecasting capabilities for enhancing flood prediction in regions prone to extreme rainfall, emphasizing its role in building more resilient and proactive flood management systems.
How to cite: Tomassetti, B., Iocca, F., Sini, F., Speranza, G., Giordano, V., Montopoli, M., Di Fabio, S., Didimi, L. G., Lazzeri, M., Tedeschini, M., Pellegrini, M., and Lombardi, A.: Nowcasting Radar for Hydrological Flood Prediction: applications in the Marche Region, Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8795, https://doi.org/10.5194/egusphere-egu25-8795, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot A
EGU25-16590 | Posters virtual | VPS10
Exploring a sustainable solid transport management strategy at local levelThu, 01 May, 14:00–15:45 (CEST) | vPA.10
At a local level, river sections maintenance represents a reduction condition of hydrological risk where soil defense work have been carried out.
In this context, this paper describes how the hydrological-hydraulic monitoring of a soil protection intervention can represent the first step for an integrated management strategy of the river ecosystem aimed at maintaining hydraulic safety at inter-municipal level and at the economic-financial sustainability of the interventions.
The case study concerns the soil defense work of - Celone valley - within the framework of agreement memorandum between the municipalities of Castelluccio Valmaggiore, Celle Di San Vito, Faeto and Troia.
The intervention received funding from the Environment Italian Ministry as part of the Puglia Development Pact. The Implementing Body was the Government Commissioner for hydrogeological risk in Puglia.
The study area is located in northern Puglia as part of Celone basin, the portion closed by Torrebianca Dam. The area is surrounded in Daunia Apennines and is characterised by provincial roads that connect the municipalities affected by flooding phenomena. Specifically, we would like to recall the flood event of 12.13.2015 in which two Danish technicians died near the SP124, overwhelmed by a flood wave.
During the above-mentioned work, solid material transport was identified as a trigger for the landslide and its controlled removal could become a sustainable management strategy.
Therefore, starting from the post-operam monitoring, a solid transport indirect monitoring was planned in order to design the controlled extraction of material and its reuse, allowing the river sections upgrading and its hydraulic safety.
Preliminary and qualitative obtained results show the feasibility and economic sustainability of project. This strategy, codesigned and shared with all stakeholders, aims to become a long-term best practice for sustainable territorial management of the river ecosystem.
How to cite: Mita, L., Doria, A., and Godano, F.: Exploring a sustainable solid transport management strategy at local level, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16590, https://doi.org/10.5194/egusphere-egu25-16590, 2025.
