Orals: Mon, 4 May, 10:45–18:00 | Room D2
The early decades of the 21st century have been marked by a profound and accelerating shift in the European hydrological cycle, demanding a fundamental re-evaluation of how areas susceptible to precipitation extremes are identified. This study presents a risk map for extreme precipitation events (EPEs), categorizing areas into four risk levels: from no risk to high risk. The study includes 70 years of historical data from E-OBS (1951-2020) and 13 bias-adjusted CORDEX models under two future scenarios, SSP2-4.5 and SSP5-8.5, for the period 2021-2100. To ensure reliable detection of long-term changes, the analysis employs the Mann-Kendall test with an iterative pre-whitening procedure.
The historical risk assessment derived from seven decades of E-OBS observational data shows a heterogeneous distribution across Europe. Elevated risk zones are predominantly concentrated along the western coastal regions of Scandinavia, particularly in Norway's Atlantic-facing territories. In contrast, large portions of the continental interior, including substantial areas of Poland, Germany, and the eastern Baltic States, exhibit medium to low risk levels. Under the SSP2-4.5 scenario, some areas may experience heightened risk of precipitation extremes, notably in Scandinavia, yet considerable uncertainty remains across models.
The SSP5-8.5 scenario presents a noticeable increase in risk levels, with widespread agreement among climate models. Almost the entire study domain transitions into medium to high-risk categories. This wholesale shift represents not merely an intensification of existing patterns but a fundamental reorganization of the region's extreme precipitation climatology. The changes are especially pronounced across the Scandinavian countries, with almost the entire region falling into the high-risk category.
The contrast between two emissions scenarios emphasizes the strong sensitivity of extreme precipitation patterns to greenhouse gas concentration pathways. These findings provide critical evidence for the necessity of urgent mitigation actions, and support adaptation planning in regions that may potentially experience heightened risk of extreme precipitation.
How to cite: Fakour, P., Ustrnul, Z., and Messori, G.: Climate Driven Risk Assessment: Identifying Susceptible Areas and Future Shifts in Extreme Precipitation Across North-Central Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-792, https://doi.org/10.5194/egusphere-egu26-792, 2026.
Due to climate change, the quantity of extreme rainfall events caused by tropical cyclones in Beijing have exhibited a significant increasing trend, posing great challenge to disaster prevention and mitigation efforts in the city. To enhance Beijing's capacity to respond to tropical cyclone-induced precipitation, this study collects multi-source data on precipitation, tropical cyclones, and precipitable water vapor. It proposes a dual-scale (temporal and spatial) quantitative identification method for tropical cyclone precipitation, generates a tropical cyclone precipitation dataset for Beijing, and compiles a dataset of 20 typical tropical cyclones. Furthermore, the study elucidates the characteristics of tropical cyclones affecting Beijing. 24 indicators reflecting the characteristics of tropical cyclone were designed based on the how tropical cyclone forms rainfall. A quantitative study on the relationship between the characteristics of tropical cyclones and the precipitation characteristics in Beijing, (i.e. the total rainfall and the maximum 1-day rainfall) was conducted. The results indicates that: 1) A increasing trend was observed in the number of tropical cyclones impacting Beijing from the 1980s to the 2010s, with indications that this trend is likely to continue. Furthermore, statistical analysis confirms that tropical cyclones making landfall in Shandong and Liaoning provinces are more likely to trigger extreme rainfall events in Beijing; 2) the correlation between the total precipitation of the tropical cyclone and the distance from the tropical cyclone centre to the centroid of Beijing shows a trigonometric distribution, and the total precipitation generally reaches to maximum at the distance of 500 km, and to minimum at the distance of 400 and 700km; 3) the maximum 1-day precipitation of the tropical cyclone and the precipitable water vapor show a significant positive correlation which is affected by the distance from the tropical cyclone centre to the centroid of Beijing; specifically, the correlation coefficient up to 0.95 when the distance is less than 500km, whereas it decreases to 0.56 when the distance exceeds 500km; 4) Regression models were constructed to quantify the relationships between the 24 characteristic indicators and the precipitation characteristics in Beijing (i.e. the total rainfall and the maximum 1-day rainfall). The models demonstrated a strong goodness-of-fit, with Pearson correlation coefficients reaching 0.90 for total rainfall and 0.83 for maximum 1-day rainfall.
How to cite: Zhang, X., Li, Y., Chen, Y., Lu, Y., Xue, Z., and Hu, X.: The Influence of Tropical Cyclone Characteristics on Rainstorm Magnitude in Beijing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6101, https://doi.org/10.5194/egusphere-egu26-6101, 2026.
Abstract
Despite the rapid intensification of extreme rainfall due to recent climate change, the Probable Maximum Precipitations (PMPs), key design standards for hydraulic structures in Korea, have not been updated for a long period. This lack of updates raises concerns that the current standards fail to reflect actual climate risks. To address this, this study proposes a future PMPs estimation procedure tailored to Korean conditions based on the WMO (2009) hydro-meteorological method. The procedure was applied to the SSP2-4.5 and SSP5-8.5 scenarios of the EC-Earth3-Veg and HadGEM3-RA models to project future ensemble-based PMPs. In particular, conventional methods calculate the moisture maximization ratio by fixing the representative precipitable water to historical observed values. This approach tends to over-reflect the increase in future maximum precipitable water, leading to an overestimation of PMPs. To overcome this limitation, this study estimated future annual representative precipitable water based on a Copula model. By applying this to the moisture maximization ratio calculation, the issue of PMPs overestimation was effectively mitigated. However, since this study relies on the results of two climate models, inherent uncertainties exist. Therefore, future research needs to project future PMPs using a multi-model ensemble with a broader range of models to enhance reliability.
Acknowledgment
This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).
How to cite: Kim, H., Lee, J., Jung, H., and KIm, S.: Projection of Future PMPs in Korea Using a Copula-Based Moisture Maximization Ratio, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9020, https://doi.org/10.5194/egusphere-egu26-9020, 2026.
Quantifying the extremeness of precipitation events in a spatio-temporally consistent manner, especially over a large river basin with hydro-climatic heterogeneity, poses a key challenge in flood risk assessment. The Mahanadi River basin (MRB) in India is located in the core monsoon region and often experiences tropical cyclone-induced severe rainfall. Extreme precipitation in the upstream sub-basins of MRB, leads to major flooding in the densely populated lower sub-basin (delta) region. Existing studies typically quantify precipitation extremes for the basin as a whole and thereby often overlook spatial heterogeneity, resulting in an underestimation of the variability and distribution of extreme rainfall across sub-basins. This study, therefore, applies the Weather Extremity Index (WEI) and its cross-scale extension (xWEI) at the sub-basin scale to further investigate flood-generating processes.
Both WEI and xWEI quantify extremeness as a function of the spatial extent and rarity (frequency) of precipitation events. However, WEI characterises events by their maximum extremeness at a specific duration of rainfall accumulation, whereas xWEI captures extremeness across the entire spatio-temporal scale. In this study, WEI is applied as an event-centred diagnostic, while xWEI is computed continuously in time for the sub-basins. Both indices are used to examine precipitation extremeness during six major flood events that occurred in the delta region since 2000.
The WEI time series shows systematically high values at medium to long accumulation durations (5–10 days) in the upstream sub-basins prior to flood events in the delta. An exception is observed for the 2006 flood, during which high WEI values occurred directly over the delta on the flood date. The absence of a consistent dominant duration in WEI over the delta suggests that local precipitation alone is not sufficient to explain flood occurrence. A similar pattern is observed in the xWEI time series, with higher values in the upstream catchments during the days preceding delta floods. In addition, xWEI highlights pronounced extremeness at short to medium durations (2–5 days) in the upstream basins. Across all six events, xWEI consistently reveals a clear upstream–downstream evolution of precipitation extremeness in the MRB, with maxima appearing in the upper sub-basin at 2–3 days before and in the middle sub-basin 1–2 days before any major flood event at delta.
How to cite: Karmakar, S., Voit, P., and Chatterjee, C.: Quantification of Precipitation Extremeness over a Large Indian River Basin using Weather Extremity Indices, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17294, https://doi.org/10.5194/egusphere-egu26-17294, 2026.
The impact of a heavy precipitation event is determined not only by the total amount of precipitation but also by its spatial and temporal distribution. This study introduces a framework to quantify the key spatio-temporal properties of precipitation events - namely their characteristic time, length, and speed - using gridded datasets.
To this end, we apply a spectral filtering approach based on wavelet decomposition. Wavelet decomposition has been proven to be highly effective in uncovering underlying frequency structures in time series and is well-suited for the analysis of two-dimensional spatial patterns. Previous applications to spatial precipitation fields (e.g., Buschow, 2024; Buschow & Friederichs, 2021) have demonstrated its potential to improve the understanding and description of precipitation events. We extend these methods to capture both spatial and temporal characteristics, providing for a comprehensive description of three-dimensional precipitation extremes.
Focusing on Germany, we analyze summer precipitation events using high-resolution datasets. These include the RadKlim dataset provided by the German Weather Service and a novel CPM ensemble, obtained from the NUKLEUS project.
We assess the physical plausibility of the derived characteristics, examine their relationships to large-scale atmospheric dynamics, and also assess their changes with ongoing climate change. Our results reveal systematic patterns in the spatio-temporal organization of precipitation extremes.
The framework presented here provides a robust tool for understanding extreme precipitation and offers potential for improved risk assessment and future climate studies. Our work is part of the BMFTR-funded ClimXtreme CoDEx project.
How to cite: Szemkus, S., Buschow, S., and Friederichs, P.: Describing the spatio-temporal structure of precipitation extremes using wavelet transformation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20065, https://doi.org/10.5194/egusphere-egu26-20065, 2026.
The storm Hans struck Eastern Norway in August 2023 with two days of intense rain, triggering what may become the country’s most expensive weather-related disaster. Remarkably, the flooding affected large rivers that normally peak during the snowmelt season from April to June. We answer the two related research questions of a) could we have anticipated a storm of Hans’ magnitude, and b) could the storm have coincided with the April to June snowmelt, causing a compound event. We leveraged the UNSEEN methodology (UNprecedented Simulated Extremes using ENsembles), using 3.5 years of extended-range weather forecasts and their 20-year reforecasts from ECMWF as well as ERA5 reanalysis. UNSEEN uses large ensembles of model simulations to assess rare but plausible extremes, which enables the assessments of return periods not possible with the short historical record. We found that while Hans is at the high end of what could be expected, the larger sample reveals a continuum of weaker but still previously unprecedented events throughout the year that remain capable of causing severe impacts. The most extreme rainfall events are most probable between July and September, when snowmelt does not amplify flooding. However, two of the most extreme simulated events occurred in May, indicating that a physically consistent compound disaster — heavy rain coinciding with snowmelt — is possible under the right conditions. Together, these results show how data-driven counterfactuals can help anticipate unprecedented extremes.
How to cite: Hellan, S. P., Dunn-Sigouin, E., Kolstad, E. W., Sauvat, E., Simpson, R., and Wulff, C. O. W.: How unlikely was the storm Hans? Reusing extended range forecasts to anticipate unprecedented extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22475, https://doi.org/10.5194/egusphere-egu26-22475, 2026.
Precipitation whiplash, defined as a rapid transition between anomalously wet and anomalously dry conditions, has emerged as an important hydroclimatic extreme under climate change, with potential implications for wildfire activity. While previous studies have linked precipitation whiplash to wildfire regimes in arid and semi-arid regions, its occurrence and impacts in monsoon-dominated East Asia remain poorly understood. This study investigates the occurrence of precipitation whiplash across South Korea and examines its relationship with interannual variability in winter wildfire damage.
Using high-resolution (500m) daily precipitation data from the Korea Meteorological Administration, Standardised Precipitation Index (SPI) values were calculated at one-month (SPI1) and three-month (SPI3) timescales. Precipitation whiplash events were identified based on SPI-based thresholds, including moderate (±1) and extreme (±1.6) definitions. In addition, a persistence-weighted whiplash intensity metric was developed to integrate anomaly magnitude and temporal reinforcement. National wildfire-damaged area for January–March during 2001–2016 was used to assess climate–fire relationships.
Results show that precipitation whiplash occurred recurrently across South Korea, with substantial interannual variability in both spatial extent and intensity. Pearson correlation analysis revealed positive associations between winter wildfire-damaged area and whiplash indicators, with the strongest relationship observed for the spatial extent of extreme short-term whiplash (SPI1-whiplash1.6; r = 0.81). Moderate positive correlations were also found for SPI1-whiplash1, SPI3-whiplash1, and national mean persistence-weighted whiplash intensity.
These findings indicate that extreme, spatially extensive wet–to-dry transitions are closely linked to enhanced winter wildfire damage in South Korea, highlighting precipitation whiplash as a relevant climate-based indicator for wildfire risk assessment in monsoon-influenced regions.
How to cite: Lee, Y., Hwang, J. H., Yoo, Y.-J., and Jeon, S.: SPI-based Assessment of Precipitation Whiplash and Its Relationshipwith Winter Wildfire Damage Area in South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8877, https://doi.org/10.5194/egusphere-egu26-8877, 2026.
This study assesses the impacts of climate change on rainfall patterns in the North-Eastern (NE) Indian state of Tripura using historical rainfall observations from the Indian Meteorological Department (IMD) and future climate projections derived from CMIP6 General Circulation Models (GCMs). The analysis quantifies changes in rainfall patterns and updates the Intensity-Duration-Frequency (IDF) curves to incorporate the effects of projected climate variability. The performance of 13 CMIP6 GCMs is evaluated through statistical analysis to assess their ability to reproduce historical precipitation patterns over the period 1984–2014. Based on this evaluation, the most suitable models are selected for projecting future rainfall behavior. The results indicate a potential shift in future rainfall patterns, with Tripura expected to experience more frequent extreme rainfall events in the future, with daily rainfall of 100mm or more. The IDF curves for the future period (2025–2054) are developed using the selected CMIP6 model outputs and compared with IDF curves derived from IMD observed rainfall data. The updated IDF curves offer valuable insights into the evolution of rainfall extremes, enhancing our understanding of climate change's impacts on rainfall-induced natural hazards in Tripura.
How to cite: Kumar, S.: Assessment of Climate Change Impacts on Rainfall Extremes and Intensity–Duration–Frequency Curves in Tripura, Northeast India, Using CMIP6 Climate Models dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20865, https://doi.org/10.5194/egusphere-egu26-20865, 2026.
On 29 October 2024, a cut-off low triggered an extreme rainfall event over eastern Spain, with daily accumulations exceeding annual precipitation in several areas. The impacts were particularly severe in the province of Valencia, where widespread totals above 300 mm were recorded and local maxima reached up to 771 mm, resulting in more than 200 fatalities. Given the magnitude of the disaster, the dynamical and thermodynamical drivers of the event, as well as the potential role of climate change, have already prompted extensive investigation. Several media reports and recent studies (Campos et al., 2025) have pointed to the presence of an upper-level tropospheric moisture plume resembling an atmospheric river (hereafter AR-like structure) connecting the Mediterranean region with the tropical Atlantic via North Africa, suggesting that it may have contributed to the event’s intensification. However, the quantitative contribution of this structure to the observed precipitation remains unclear.
Here we address this issue using the WRF model coupled with Water Vapor Tracers (WRF-WVTs), which allows tracking moisture from prescribed source regions to precipitation while fully resolving the event dynamics. Our results show that the Mediterranean Sea was the dominant direct moisture source, while moisture associated with the AR-like structure contributed approximately 20-30% of the total precipitation. To further assess the role of this remote moisture transport, we introduce a methodology to quantify its indirect impact through enhanced latent heat release and the resulting increase in atmospheric instability. We find that this indirect mechanism is substantially more important than the direct moisture contribution, highlighting the key role of the AR-like structure in intensifying the event.
Campos, D. A., Grayson, K., Saurral, R. I., Beyer, S., John, A., Olmo, M., and Doblas-Reyes, F.: The October 2024 Extreme Precipitation Event over Valencia: Storyline Attribution of the Synoptic-Scale Thermodynamic Drivers, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-5929, 2025.
How to cite: Crespo-Otero, A., Insua-Costa, D., and Míguez-Macho, G.: On the role of an AR-like moisture plume in the October 2024 Valencia extreme rainfall event, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9499, https://doi.org/10.5194/egusphere-egu26-9499, 2026.
Most climate change impact assessments focusing on floods rely on daily resolution data. For snow-influenced catchments, these assessments typically project either decreases or no changes in flood magnitude and frequency because decreases in snowmelt can compensate for increases in extreme precipitation. Yet, sub-daily rainfall extremes will intensify more strongly than daily rainfall extremes under climate change. This suggests that a daily resolution may be insufficient for studying future flood responses to rainfall intensification. In this study, we show how moving from daily to hourly resolution data reshapes our understanding of how floods will change in a warming climate.
We find that, in more than 75% of the Alpine rivers studied, daily streamflow projections underestimate the magnitude and recurrence rate of the 100-yearly flood compared to hourly projections. While hourly projections show increases in flood magnitudes in strongly snow-influenced basins, daily projections point to decreasing flood magnitudes in the future. Under a high emission scenario, these differences in the sign of change between hourly and daily projections become significant before mid-century in more than half of the catchments. The flood seasonality signal also differs between daily and hourly projections for snow-influenced catchments: daily projections show a clearly earlier onset of floods compared to the historical period, whereas this signal is weak in hourly projections. While the daily perspective suggests a reduction in the magnitude of extreme floods due to a decrease in the magnitude of extreme snowmelt events in the future, the hourly perspective indicates that intensified sub-daily precipitation can compensate for snowmelt decline.
These results highlight that using daily instead of hourly projections may lead to wrong conclusions on changes in flooding in a warming climate in terms of the magnitude of change and, in snow-influenced catchments, even in terms of the sign of change. Using hourly resolution data is therefore good practice to guide adaptation strategies to flood response to climate warming.
How to cite: Astagneau, P. C., Wood, R. R., and Brunner, M. I.: Time resolution changes perspective on flood responses to climate warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20501, https://doi.org/10.5194/egusphere-egu26-20501, 2026.
On 29 October 2024, an exceptional hydro-meteorological event impacted eastern Spain, producing extreme rainfall accumulations and widespread flash flooding, with the most severe consequences recorded in the Valencian Community. The event resulted in 238 fatalities nationwide, of which 230 occurred in the province of Valencia, and caused economic losses estimated at approximately €17 billion. More than 80% of the fatalities were concentrated in the southern Valencian Metropolitan Area, highlighting its extreme vulnerability to compound rainfall-runoff processes. This area is a highly urbanized Mediterranean lowland located south of the city of Valencia (Spain), draining an area of approximately 530 km² through a dense network of ephemeral streams. These catchments are characterized by short response times, steep upstream slopes, permeable lithologies, and limited natural floodplain storage. Downstream of natural flood-lamination areas such as the Pla de Quart, river channels intersect densely populated municipalities, where urban expansion has progressively encroached upon flood-prone areas, substantially increasing exposure and, consequently, flood risk.
This contribution presents a detailed reconstruction of this catastrophic flooding, integrating meteorological analysis, distributed hydrological modelling, and high-resolution hydraulic simulations. Rainfall reconstruction was performed using an extensive rain-gauge network across the Valencian Community, capturing the strong spatial variability and temporal clustering of the event. The storm evolved through two clearly differentiated phases: an initial morning rainfall episode that led to widespread soil saturation across the catchments, followed by an afternoon-evening phase characterized by extraordinary rainfall intensities and persistence.
Hydrological and sediment transport simulations were conducted to represent both the generation and propagation of runoff and solid load throughout the catchment, using the fully distributed eco-hydrological model TETIS. The modelling framework combined long-term daily simulations to establish realistic antecedent conditions with event-scale subdaily simulations at 10-minute resolution. This approach enabled the reconstruction of hydrographs and sediment fluxes for the main tributaries upstream of flood-lamination zones, prior to the occurrence of major overbank flooding. It also allowed the estimation of peak discharges that largely exceeded the instrumental record, highlighting the significant contribution of solid load to the total flood volume.
The resulting hydrographs (with a combined peak of 7,500 m3/s) were used as boundary conditions for two-dimensional hydraulic modelling, allowing the reproduction of flood propagation and inundation patterns in highly urbanized areas of southern Valencian Metropolitan Area. Results show that flood depths and impacts were strongly amplified by floodplain anthropization associated with urban expansion, leading to water levels substantially higher than those expected under more natural conditions.
This integrated reconstruction improves the understanding of the coupled meteorological and hydrological processes that controlled the October 2024 flood and provides a physically consistent basis for assessing flood hazards in highly urbanized Mediterranean catchments.
How to cite: Beneyto, C., Cachay-Melly, J. A., Aranda, J. Á., Eguibar, M. Á., and Francés, F.: Reconstruction of the October 2024 catastrophic flooding in the southern Valencia Metropolitan Area, Spain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11620, https://doi.org/10.5194/egusphere-egu26-11620, 2026.
In June 2024, the Muhuri River in eastern Bangladesh reportedly rose by ~7 m within three days, exceeding danger levels by >3 m and affecting more than 3.6 million people across 11 districts. Severe monsoon flooding in August 2024 reinforced the vulnerability of transboundary, data-scarce catchments to extreme hydrological hazards. To assess future flash-flood risks in the Muhuri River Basin, a four-tank conceptual rainfall–runoff model was developed and calibrated against daily discharge at Parshuram station for 2010–2025 (KGE = 0.72; PBIAS = 5.78%; RMSE = 14.30). The calibrated model was forced with an ensemble of 15 bias-corrected CMIP6 General Circulation Models under SSP2-4.5 and SSP5-8.5. Bias correction used Empirical Quantile Mapping applied to precipitation, maximum temperature, and minimum temperature. The projections were analyzed for near- (2026–2050), mid- (2051–2075), and late-century (2076–2100) periods relative to a historical baseline (1985–2014). The results suggest intensification of high-flow hazards: the 95th-percentile daily high flow (Q95) increases by up to 20.7% under SSP5-8.5, and the frequency of Q95 exceedance events increases by 69%. It is also noted that October discharge (post-monsoon) increases by 28.6%, consistent with delayed recession and a higher likelihood of prolonged inundation. The extreme-event analysis further suggests that 100-year flood magnitudes may increase by up to 22%, with substantial inter-model spread. Our data further indicates measurable reorganization of seasonal flow regimes under future forcing, consistent with emerging non-stationary flood behavior. Overall, these findings support climate-informed, impact-based early warning and adaptation planning for vulnerable transboundary basins.
How to cite: Rokonuzzaman, M., Sadat Khan, N., and Tanim Siddiquie, K.: Climate-Driven Intensification of Flash-Flood Hazards in Transboundary Muhuri River basin: A CMIP6-Based Assessment Using the Tank Hydrological Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4504, https://doi.org/10.5194/egusphere-egu26-4504, 2026.
Global flood reporting has generally improved over the last century, particularly as global disaster
databases have started archiving and aggregating flood events. While these disaster databases,
including EM-DAT, DFO, HANZE, and UNDRR, are helpful tools for flood analysis, they are
often incomplete in their reporting. Therefore, aggregating them to form a more complete database
is critical. It is equally important when training a disaster-level flood event model using these
databases to account for systemic inequality in flood reporting. Here, we present a three-phase
INLA-SPDE flood prediction model which determines relevant climate signals from documented
flood events, determines which countries report floods the most consistently relative to the climate
signals, and projects global latent flood risk on a monthly 0.25-degree scale using the best reporting
countries for calibration. The result is a spatial risk field which ranks relative risk of disaster-level
flood events based on climate conditions one month in advance (sub-seasonal timescale).
Predictions at the 0.25-degree level perform well at relative risk ranking (AUC = 81.7 %) but
returns many false positives due to the complexity and rarity of these events (Precision = 6.12 %).
However, when aggregating predictions to the country level, this issue is minimized (Precision =
49.0 %), meaning country level predictions may be used to determine monthly likelihood of a flood
event occurring, while cell level predictions may be used to determine high risk locations within
the country. Combined, this modeling methodology allows for prediction of floods beyond the
capabilities of normal disaster database models by separating flood reporting biases from latent
climate signals as much as possible.
How to cite: Magers, B., Najibi, N., and Devineni, N.: Employing integrated nested laplace approximation with stochastic partial differential equation (INLA-SPDE) modeling for sub-seasonal flood prediction using a globally aggregated disaster database, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5588, https://doi.org/10.5194/egusphere-egu26-5588, 2026.
This study investigates the relationship between Euro-Atlantic large-scale atmospheric circulation, characterized using year-round weather regimes (WRs), and major flood events in the Greater Alpine Region (GAR). With the goal of characterizing atmospheric conditions leading to major flood events and therefore gaining insights on their predictability, we identify the WR paths most commonly linked to these events in the GAR.
To this aim, we identify on average one major flood event per year over the GAR in the period 1951-2023 using discharge simulations from a regionalized rainfall-runoff model (a modified version of the TUW model) for the region. Of all the events identified, those selected were chosen based on three features: spatial extent, duration and intensity. These events cover large portions of the GAR, allowing the exploration of the role of large-scale atmospheric dynamics rather than local or convective processes.
WRs classification is based on daily geopotential height data at 500 hPa (Z500) from ERA5 reanalysis, following the methodology outlined by Grams et al. (2017), that enables the year-round characterization of atmospheric patterns.
Given the prevalence of regional floods during autumn, our analysis focuses on SON (September–October–November). We link flood events to their corresponding WRs at the time window included between peak day and two days before and compare their frequency of occurrence against the seasonal WRs climatology to isolate statistically significant associations. Results reveal that floods occur predominantly under Scandinavian Blocking (ScBL) and Scandinavian Trough (ScTr) regimes, which favor events of large-scale precipitation that can, in turn, lead to flood events during this season. Categorizing their magnitude using a quantile-based approach, we observe that precipitation events happening in proximity of floods associated with ScTr and ScBL regimes can be classified as extremes (magnitude exceeding 95th quantile). This result is also supported by the integrated water vapor (IVT) analysis, showing the presence of a larger south-north vapor transport from the Mediterranean basin towards the Alps during flood events when compared to the characteristic IVT patterns of ScBL and ScTr regimes.
Linking autumn floods over the GAR with specific large-scale atmospheric circulation regimes would support the potential use of WRs diagnostics as predictive tools to improve early warning systems for extreme hydrological events at present and, in principle, under future climate scenarios.
How to cite: Tessari, I., Kumar, V., Basso, A., Lombardo, L., Giuntoli, I., Corti, S., Arnone, E., and Viglione, A.: Linking Euro-Atlantic Weather Regimes to Precipitation Patterns and Major Regional Flood Events in the Greater Alpine Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9570, https://doi.org/10.5194/egusphere-egu26-9570, 2026.
Floods can have enhanced impacts when they occur in close succession with streamflow droughts. Despite the increased likelihood of impacts during such drought-to-flood transition events, substantial gaps remain in the understanding of the hydrological processes and drivers leading to their development. Specifically, it remains unclear how changes in hydrological conditions (IHCs) after drought and meteorological forcings during the transition towards flood conditions propagate to flood characteristics, such as duration and peak flow. We address this research gap by running three stress test experiments at the event scale: (1) changes in IHC based on perturbations applied to the meteorological forcing (e.g., temperature and precipitation) during the drought; (2) perturbation of observed forcing during the transition; and (3) modifications to both IHC and forcings during the event (i.e., a combination of cases 1 and 2). To do so, we calibrate the bucket-type GR4J hydrological model across a quasi-global dataset encompassing 2003 catchments. Using the results of all synthetic experiments conducted across all catchments, we estimate the isolated and joint sensitivities of both transition and flood characteristics to changes in drought characteristics (e.g., duration, intensity, severity) and to meteorological forcings during the transition. By analyzing the joint sensitivity of a given attribute (e.g., peak flow) to IHCs and meteorological forcings, we estimate the relative importance of each in driving the attribute's overall sensitivity (i.e., total effect of all individual sensitivities). Our findings indicate that, regardless of whether the system is stressed in isolation (i.e., experiment 1 or 2) or jointly (i.e., experiment 3), meteorological forcings - primarily temperature - are the main drivers affecting changes in transition and flood characteristics across hydroclimatic regions. Overall, clear differences between sensitivities, e.g., of peak flow to precipitation during the transition, emerge when comparing snowmelt- and rainfall-influenced catchments. Furthermore, hydroclimatic and streamflow regimes, along with catchment storage dynamics, are the main proxies for understanding the sensitivity of transition attributes to changes in IHC and meteorological forcings. Additionally, we show that IHCs are more important than meteorological forcing in explaining the variability of transition characteristics. Consequently, although IHCs control the baseline state, small changes in variables such as temperature during transitions can significantly alter flood characteristics, largely independently of the IHCs. Ultimately, we show that warming, rather than variations in drought severity (i.e., antecedent conditions) or precipitation, is likely to shape future transitional floods. Our results enhance our understanding of drought-to-flood transitions and their sensitivities to hydrometeorological conditions, thereby generating clearer insights into how droughts and meteorology influence floods and their impacts.
How to cite: Muñoz-Castro, E., Anderson, B. J., Swain, D. L., and Brunner, M. I.: How do drought conditions and meteorology shape subsequent floods? Insights from quasi-global stress-test experiments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13390, https://doi.org/10.5194/egusphere-egu26-13390, 2026.
ABSTRACT
Floods are a major natural disaster that occurs suddenly, causing loss of life and significant socioeconomic damage, making proactive response through accurate forecasting essential. River water levels are a key indicator for determining flood occurrence, but forecasting accuracy varies depending on hydrological, meteorological, and watershed characteristics. In particular, small-scale rivers have higher water level variability compared to large-scale rivers, limiting their practical flood response capabilities. To overcome these limitations, this study aimed to develop an AI-based river water level forecasting model and improve its forecasting performance. The forecast model is configured to forecast river water levels three hours ahead using time-series data from the previous 24 hours as input, based on the forecast time point. Four locations among existing AI-forecast rivers where flood forecasting is difficult were selected, and input variables reflecting each location's hydrological, meteorological, and oceanographic characteristics were configured. As a result, this study confirmed a trend toward improved flood forecasting performance through the configuration of input variables tailored to each site's characteristics and the adoption of the latest AI models.
Acknowledgments
This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).
How to cite: Jung, H., Kim, H., Lee, J., and Kim, S.: Development and Performance Improvement of a Time-Series-Based AI Model for Flood Forecasting and Warning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9017, https://doi.org/10.5194/egusphere-egu26-9017, 2026.
Flood generation arises from complex, scale-dependent processes that vary across global river catchments. Understanding and predicting the controls on flood generation is key in obtaining robust estimations of flood frequency, particularly for ungauged basins. Indeed, reliable design-flood estimates are fundamental for flood risk assessment, infrastructure design as well as understanding riverine geomorphology and ecology. While recent advances have improved global flood estimation, driven largely by improved hydrological understanding and expanded data availability, key drivers of extreme floods, including compound processes and scale-dependent effects, are still insufficiently represented, and model performance has rarely been evaluated systematically across hydro-climatic regions and flood magnitudes. Here, we leverage recent advances in global river gauge records, high-resolution river hydrography, and comprehensive catchment attribute datasets in order to develop a machine learning model for estimating design floods in ungauged rivers worldwide. We generate a global design-flood dataset and conduct a systematic evaluation of model performance across hydro-climatic regions and flood magnitudes, benchmarking against existing global design-flood products. Using interpretable machine-learning techniques, we identify dominant controls on flood generation and demonstrate how basin classification can inform flood estimation from moderate to extreme events. Our results reveal strong region-specific controls on flood extremes and provide new insights for improving design-flood estimation frameworks worldwide.
How to cite: Liu, Y., Slater, L., Moulds, S., Wortmann, M., Zhang, B., Gu, X., and Parsons, D.: Controls on river flood generation: implications for design floods , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4055, https://doi.org/10.5194/egusphere-egu26-4055, 2026.
As global temperatures rise, the spatial and temporal patterns of weather hazards are shifting, increasingly affecting industries vital to economic growth. Shipping, the backbone of global trade, is particularly susceptible, yet inland waterways—crucial for linking domestic regions to international markets—have received insufficient attention. This study presents the first quantitative assessment of how weather hazards impact inland waterway operations, focusing on China’s Yangtze River and Grand Canal system, the world’s busiest inland waterways. Between 2000 and 2024, these waterways averaged 301 suitable navigation days per year, marking an overall improvement over the preceding two decades, largely attributed to a 55% decline in strong wind events, from ~31 to ~14 days annually. However, low visibility remains the dominant constraint on navigability, causing ~44 days of disruption annually, with fog and haze emerging as the primary contributors. Heavy rainfall, intensifying with warming, leads to ~10 days of disruption, ranking as the third major hazard to navigation. Simulations suggest that improving navigation technologies, such as lowering visibility requirements from 2,000 meters to 1000 or 500 meters, could extend navigability on the Lower Yangtze River by 25 to 35 days annually, enhancing operational efficiency and fostering economic growth.
How to cite: Sun, S.: Weather Hazards Dynamically Reshape Navigability in China's Inland Waterways, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18834, https://doi.org/10.5194/egusphere-egu26-18834, 2026.
Snow disasters, intensified by climate change, pose increasing threats to infrastructure and socio-economic systems. However, conventional risk assessments often rely heavily on meteorological hazards or static topographic factors, frequently overlooking the critical role of exposure in urban environments. This study introduces the Maximum Disaster Spatial Density (MDSD) method, a novel optimization framework designed to identify snow disaster-prone areas by integrating historical disaster records (2009–2018) with climatic, topographic, and social variables. Using datasets covering South Korea, including radar-based precipitation, temperature, MODIS-based Normalized Difference Snow Index (NDSI), elevation, and building density, the MDSD algorithm iteratively optimizes feature weights to maximize disaster density within high-risk clusters. Our analysis reveals that precipitation and building density are the dominant determinants of snow disaster vulnerability, whereas elevation and satellite-based snow cover duration show relatively lower importance. This finding challenges the traditional assumption that high-altitude mountainous regions are inherently more vulnerable, quantitatively demonstrating that disaster risk is driven by the intersection of extreme weather and built-environment exposure. To address model uncertainty, we applied an ensemble approach with 20 realizations, generating a probabilistic snow disaster risk map (0–1 scale). This map effectively highlights high-risk zones, including coastal urban areas, which were previously underestimated by topography-based assessments. Furthermore, we propose a dual-track disaster response strategy by integrating this static risk map as a priority filter into a real-time monitoring system. This framework enables decision-makers to prioritize resource allocation to high-exposure areas during extreme snow events, bridging the gap between scientific risk assessment and practical disaster management.
Acknowledgements
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00518650).
How to cite: Park, J., Kim, S., Kim, D., Lee, J., and Bateni, S. M.: Development of a Probabilistic Snow Disaster Risk Assessment Framework Integrating Hazard and Exposure: The Maximum Disaster Spatial Density (MDSD) Optimization Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16332, https://doi.org/10.5194/egusphere-egu26-16332, 2026.
Hailstorms are known to cause great damage to agriculture and infrastructure. However, understanding and identifying the conditions favorable to hail remains a major challenge due to the complex, multi-scale nature of deep convective processes. In this study, we investigate the use of a W-Net convolutional neural network (CNN), which proved successful in image segmentation tasks, to identify atmospheric environments susceptible to hail from high-resolution numerical weather prediction data. The NOAA High-resolution Rapid Refresh (HRRR) model explicitly resolves convective processes owing to its fine spatial (3 km) and temporal (hourly) resolution. We consider meteorological variables from HRRR outputs relevant to deep convection and hail as input features for the W-Net model. Together with hail reports across the United States for the past ten years, we construct a deep learning framework. The trained network learns spatial patterns associated with hail-prone environments and produces gridded probability maps of hail occurrence. This data-driven approach shows the potential of deep learning methods for identification of hazardous convective weather. Once trained, the model can be applied to other regions, provided that the sub-daily, high-resolution meteorological fields are available.
How to cite: Hercigonja, L., Aslam, Z., Ashfaq, M., and Telišman Prtenjak, M.: Detecting hail-prone environments using a W-Net convolutional neural network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9179, https://doi.org/10.5194/egusphere-egu26-9179, 2026.
Over the past decade, hail has been responsible for most financial losses associated with severe convective storms, with costs steadily increasing. As the population grows and increasingly invests in vulnerable infrastructure, the risk of economic damage from large hail also rises. Accurate hazard assessment is therefore essential for effective mitigation.
Report-based hail climatologies are limited by observational biases, resulting in large uncertainties, particularly in sparsely populated areas. While recent machine learning approaches have enabled the development of hail climatologies for hazard assessment, many existing datasets remain limited by regional biases and coarse spatial and/or temporal resolution.
Here, we present a novel, high-resolution large hail (> 2.5 cm in diameter) hazard dataset on a 4 km × 4 km, half-hourly grid over the contiguous United States (CONUS), ranging from 2000 to 2022. This unprecedented spatiotemporal resolution is enabled by integrating hail reports with multiple high-resolution remotely sensed hail-proxy observations, including radar reflectivity, satellite-derived brightness temperature, and total ice water path, together with key hail-relevant environmental parameters from the ERA5 reanalysis. The large hail hazard model, which we trained to produce the dataset, is based on the gradient-boosted decision tree algorithm XGBoost and provides increased spatial and temporal detail relative to prior large hail climatologies.
When evaluated on held-out test data, the model accurately reproduces the interannual, seasonal, and diurnal cycles of large hail. It resolves fine-scale topographic influences and captures coherent hail tracks. Performance is strongest for typical events, while rarer, atypical cases present a trade-off between improved detection and increased false positives.
The resulting dataset provides a high-resolution basis for large hail risk assessment across the CONUS. Because it relies on globally available satellite observations and reanalysis, the framework is transferable to other regions and can be applied to kilometer-scale weather and climate model output.
How to cite: Koch, R., Prein, A., Lohmann, U., and Aellen, N.: A Machine Learning Based High-Resolution Large Hail Climatology for the Contiguous United States, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2305, https://doi.org/10.5194/egusphere-egu26-2305, 2026.
Coastal megacities are increasingly exposed to estuarine saltwater intrusion (SWI) as a result of climate change and extreme hydro-meteorological events. Shanghai, a water-stressed megacity with a population exceeding 24 million, relies on the Yangtze Estuary for more than 80% of its freshwater supply, rendering it highly sensitive to saltwater intrusion. In this study, we apply the three-dimensional hydrodynamic model UFDECOM-i, developed by our research group based on the ECOM-si. The model incorporates two-way nested unstructured quadrilateral grids, enabling an improved representation of complex estuarine bathymetry and hydrodynamic interactions among multiple bifurcated channels during extreme conditions.
We first simulate the extreme SWI event that occurred in the summer of 2022, driven by a basin-wide record-breaking megadrought. Our simulations show that the concurrence of exceptionally low river discharge and persistent northerly winds led to severe summer salinization in the estuary. As a consequence, the Qingcaosha Reservoir experienced 98 consecutive days during which water was unfit for intake, far beyond its designed operational resilience. Further diagnostic analysis reveals that reduced river discharge weakened the seaward freshwater flushing, while wind-induced landward Ekman transport generated an anomalous horizontal estuarine circulation. This circulation pattern is characterized by inflow through the North Channel and outflow through the South Channel, and it intensified the direct transport of high-salinity water toward critical water intake locations.
In addition, we quantitatively assess the influence of future sea-level rise, SLR, on SWI intensity. Simulations under a 1.17 m SLR scenario derived from RCP8.5 projections reveal a pronounced non-linear amplification of saltwater intrusion intensity. The sensitivity of estuarine salinity to river discharge reduction increases significantly. Notably, the impact of SLR on salinity enhancement is approximately twice that of discharge decline. Under the 1.17 m SLR scenario, the duration of water unfit for intake at the Chenhang Reservoir increases from 76.17 days to 115.49 days, corresponding to a 51.6% increase from present-day levels. These results provide a quantitative basis for assessing future freshwater security in the Yangtze Estuary and offer scientific support for adaptive water resource management and refined diversion strategies under a changing climate, aligning with the United Nations Early Warnings for All initiative aimed at strengthening climate-resilient water security.
How to cite: yining, W.: Extreme Saltwater Intrusion in the Yangtze Estuary under Compound Drought, Wind Forcing, and Sea-Level Rise, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10504, https://doi.org/10.5194/egusphere-egu26-10504, 2026.
Growth in satellite observations and modeling capabilities has transformed drought monitoring by enabling near real-time situational awareness. Yet many operational efforts still emphasize hazards rather than impacts, and they often miss the compound and cascading risks that frequently accompany drought, including heatwaves, wildfires, floods, and debris flows. In this presentation, we first introduce a real-time drought monitoring and seasonal prediction system that integrates diverse data streams with AI-based algorithms for drought forecasting (https://drought.eng.uci.edu/). We then describe how drought information can be expanded beyond hazard metrics by incorporating impact and vulnerability data to support impact-based assessment of extremes and decision-relevant risk insights (https://water.eng.uci.edu/). Using several examples, we argue for an impact-centered drought monitoring paradigm that links hydroclimate conditions to physical and societal outcomes, such as crop yield losses, food insecurity, energy production disruptions, and labor impacts. We also highlight key challenges that must be addressed to make this approach operational, including inconsistent and incomplete drought impact records, limited Information about local water management and human interventions (e.g., demand, intra- and inter-basin transfers, pumping, and withdrawals), and persistent gaps between impact models and existing drought monitoring workflows. Finally, we discuss anthropogenic drought as a framing concept and show how impact-based drought analysis can be strengthened by representing drought as a coupled climate–human phenomenon rather than a purely climatic hazard.
How to cite: AghaKouchak, A., Nguyen, P., Ung, T., de Oliveira, D., Hjelmstad, A., Massing, J., Aljohani, A., Love, C., Mirchi, A., Feldman, D. L., Placht, D., and Najib, D.: From Hazard to Consequence: Impact-Based Drought Monitoring and Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13488, https://doi.org/10.5194/egusphere-egu26-13488, 2026.
Posters on site: Mon, 4 May, 08:30–10:15 | Hall X3
The magnificent but complex topography of the Alps makes them Europe's indispensable water tower, channeling vital precipitation into rivers that sustain communities, economies, and ecosystems downstream. Therefore, gaining an understanding of how precipitation behaves in this mountain region is crucial. These mountain regions are home to a great variety of climatic regimes, from Mediterranean and Atlantic maritime to strongly continental, and yet, hydro-meteorological extremes pose an increasingly disastrous threat, heightening pressure on water security, infrastructure resilience, and transboundary risk management.
There has been much research into precipitation extremes that has taken place over various parts of the Alps, bringing to light their intensity, frequency, and dynamics. However, these studies are confined to specific regions of the mountain range. The novelty of this work is threefold: a) It is based on a consistent, high-resolution, observation-based, recently published alpine-wide database of High-Impact Precipitation Events (HIPEs), observed for the period 1961-2022 and developed through a homogeneous transnational approach (Lemus-Canovas et al., 2025); b) It is based on a multi-metric analysis to draw a comprehensive picture of changes in the HIPE features, including the assessment of their spatial extent, which has not been addressed before; and c) The variations in the different characteristics of these events, as well as the responsible drivers, are addressed by considering the contribution of the annual frequency of weather types.
By combining Regression Analysis with the modeling of the Generalized Extreme Value distribution, the study was able to identify significant patterns of increase in HIPE occurrences in subregions of the Alps, along with different contributions from the main weather types when explaining the annual variability of the different characteristics of HIPEs. Such techniques offer a nuanced view of the contribution of atmospheric dynamics and local factors in determining the observed variability of precipitation extremes.
Overall, our results indicate that the emerging shifts in HIPE behavior will most likely result from a nonlinear interaction of thermodynamic intensification and dynamics of circulation. The findings are intended to emphasize the emerging regional vulnerabilities and provide science-based support for cross-border preparedness for flooding and adaptation to climate change in Alpine environments.
Keywords: High-Impact Precipitation Events (HIPEs), European Alps, trend analysis, Alpine-wide observations
Reference: Lemus-Canovas, M. (2025). High-Impact Precipitation Events in the European Alps (1961–2022) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17047822
How to cite: Gagnani, B., Lemus i Cánovas, M., and Crespi, A.: Understanding the observed changes in High-Impact Precipitation Events across the European Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1064, https://doi.org/10.5194/egusphere-egu26-1064, 2026.
Extreme precipitation is a natural hazard with significant socioeconomic implications, namely for sectors such as agriculture, including viticulture. This study provides the first comprehensive analysis of extreme precipitation events in mainland Portugal, based on sub-hourly observations. Using 10-minute precipitation data from 71 weather stations for the period 2000 to 2022, we assess the spatial and temporal variability of these events, including their seasonality, diurnal cycle, and synoptic-scale drivers. The mean seasonal contribution of extreme precipitation to total annual precipitation, defined using thresholds of 10–20 mm h-1 (yellow warnings) and >20 mm h-1 (orange and red warnings) following the criteria of the Portuguese Weather Service, is highest in winter, indicating a stronger influence of intense precipitation on annual totals. This contribution decreases in autumn and spring, reaching its minimum in summer. Extreme precipitation events occur most frequently between September and December, with a secondary maximum in April and May, particularly in the Alentejo region. The diurnal cycle exhibits a pronounced afternoon peak, consistent with convectively driven thunderstorms. In spring and summer, extreme events tend to account for a larger fraction of daily precipitation totals. Two extreme events were selected not only as case studies of heavy precipitation, hail and lightning but also as examples of understanding the specific weather conditions and atmospheric dynamics associated with such severe weather patterns. In the first case, the event on 28 May 2018 in the Douro region was associated with a cut-off low, whereas in the second case, the event on 14 September 2021 in the Alentejo region was associated with a frontal system in the final phase of its life cycle. ERA5 instability indices show a good agreement with observed lightning patterns. These results, particularly at the regional scale, provide valuable insights for climate research and socio-economic sectors such as viticulture, where extreme precipitation and hailfall pose significant risks. Acknowledgements: Research funded by Vine & Wine Portugal–Driving Sustainable Growth Through Smart Innovation, PRR & NextGeneration EU, Agendas Mobilizadoras para a Reindustrialização, Contract Nb. C644866286-011. The authors acknowledge National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2025: Centre for the Research and Technology of Agro-Environmental and Biological Sciences (https://doi.org/10.54499/UID/04033/2025) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020).
How to cite: Cruz, J., Belo-Pereira, M., Fonseca, A., and Santos, J. A.: Characterising Hourly Extreme Precipitation in Portugal: Spatial–Temporal Variability and Case Studies in Two Wine Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3539, https://doi.org/10.5194/egusphere-egu26-3539, 2026.
Estimating sub-daily precipitation return values at the kilometer-scale is critical for climate-risk assessments. However, this remains a challenge due to the strong intermittency of extremes, heterogeneous observational networks, and the breakdown of stationarity assumptions under climate change. Furthermore, coarse-resolution global climate models (e.g., CMIP6) systematically underestimate local extremes by smoothing convective processes and orographic gradients, thereby blurring hotspots that are critical for impact modelling.
We present an innovative statistical downscaling method that repurposes Areal Reduction Factors (ARFs), traditionally used to relate point rainfall to areal averages, as a resolution-bridging tool for extreme precipitation. By comparing return values derived from high-resolution (COMEPHORE, ~1 km2) and coarse-resolution (CERRA, ~25 km2) reanalyses, we compute spatially varying ARF maps. These maps quantify the attenuation of extremes induced by coarse spatial resolution and serve as multiplicative scaling factors to translate coarse-resolution outputs into 1-km products while preserving the large-scale climate signal.
This framework is validated against independent rain-gauge observations across multiple return periods and seasons. Finally, we apply the method to CMIP6 simulations to generate 3-hourly, 1-km precipitation return values for both historical and future periods.
This approach provides a computationally efficient and climate-change-consistent pathway to generate high-resolution hazard metrics, without the prohibitive cost of convection-permitting regional climate simulations.
How to cite: Droin, C., Lambert, A., Terrier, M., and Troin, M.: An ARF-based method to downscale sub-daily extreme precipitation return values, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7863, https://doi.org/10.5194/egusphere-egu26-7863, 2026.
ClimXtreme is a research programme funded by the German Federal Ministry of Research, Technology and Space (BMFTR) that comprises 25 individual projects and aims to improve understanding of European extreme weather events and associated uncertainties under anthropogenic climate change. As part of the cross‑project collaboration, a coordinated approach is being established to initiate and sustain targeted stakeholder communication and support it throughout the project period. The aim is to develop information, data and tools targeted to stakeholder needs. To this end, Hazard‑specific Stakeholder Interaction (HaSSi) groups coordinate collaborative work on windstorms, heavy precipitation, and heat/drought. This contribution shows the manifold approaches and perspectives of our research programme to better understand heavy precipitation events in a changing climate.
We use observations and climate model data, including large ensembles from global- to kilometre-scale resolution. This improves the understanding of physical processes and scale dependency. Large ensembles also help to deal with the uncertainty of climate change signals across multiple models. Characteristics of extreme precipitation can be analysed by object-oriented approaches. It allows to assess whether climate change will lead to events that will cover larger areas, last longer or travel larger distances. Newly developed statistical methods help to deal with uncertainties and the issue of short data series when investigating extremes. This includes models of spatio-temporal structures of precipitation extremes as well as further developments of Intensity-Duration-Frequency (IDF) relation, where parameters to estimate the IDF relation are functions of large scale variables. To assess impacts of heavy precipitation events, hydrological models for different catchment sizes are applied.
This allows ClimXtreme to perform research on various precipitation extremes under climate change as well as to assess individual events as case studies from a variety of perspectives as done for example for a precipitation extreme in June 2024 leading to severe flooding in southern Germany [1]. The study clearly shows that quantities of extremity strongly depend on the exact measure and the methodology used. A comprehensive view on these events is crucial especially when communicating results to stakeholders.
[1] http://dx.doi.org/10.17169/refubium-44009
How to cite: Grieger, J., Ruff, F., Forster, C., Fauer, F., Feldmann, H., Fischer-Frenzel, P., Friederichs, P., Haufs, E., Lucio-Eceiza, E. E., Meredith, E. P., Merkes, S. T., Pinto, J. G., Schröter, J., Szemkus, S., Tonn, M., Ulbrich, U., Vlachopoulos, O., Voit, P., Vorogushyn, S., and Zimmermann, T.: ClimXtreme addressing heavy precipitation events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19066, https://doi.org/10.5194/egusphere-egu26-19066, 2026.
In recent years, the impact of extreme weather events, such as droughts, has been reported more frequently in Central and Eastern European countries. Rising temperatures and increasingly variable precipitation patterns affect natural ecosystems and agricultural areas, which could impact water resources, agricultural productivity and livelihoods in the future.
This study analyses the Standardised Precipitation Index (SPI), derived from Climate Hazards Centre Infrared Precipitation with Stations (CHIRPS) data, over the last 20 years. The regional focus is on Central and Eastern European countries, with a spatial gradient from the Baltic Sea to the Adriatic Sea in order to cover several biogeographic regions.
The SPI is a widely used tool for assessing the severity and frequency of drought events, providing a standardised measure of precipitation anomalies over various temporal and spatial scales. This study uses SPI-1 and SPI-3 data (at 1- and 3-month scales) for Poland, the Czech Republic, Slovakia, Slovenia, Hungary and Croatia between 2005 and 2024. The aim of analysing these data is to initially identify spatial and temporal patterns in drought frequency, duration, and intensity for this period.
Our future collaborative work will assess the relationship between analysed precipitation patterns and other key climatic factors (e.g. temperature) and further environmental factors (e.g. soil, water and vegetation conditions). This can provide a deeper understanding of the spatial and temporal distribution of climatic extreme events in this region. Furthermore, SPI analyses should be complemented by climate projections to provide insights into potential future changes in precipitation patterns and the frequency of extreme events in Central and Eastern Europe.
How to cite: Huth, J., Hordofa, A. T., Kropacek, J., Nagy, A., Habel, M., Mukanov, B., Maerker, M., and Bachofer, F.: Spatiotemporal variability of precipitation in Central and Eastern European countries: A 20-year time series analysis of CHIRPS data for the detection of extreme events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19630, https://doi.org/10.5194/egusphere-egu26-19630, 2026.
ClimXtreme is a research programme funded by the German Federal Ministry of Research, Technology and Space (BMFTR) that comprises 25 individual projects and aims to improve understanding of European extreme weather events and associated uncertainties under anthropogenic climate change. As part of the cross‑project collaboration, a coordinated approach is being established to initiate and sustain targeted stakeholder communication and support it throughout the project period. The aim is to develop information, data and tools targeted to stakeholder needs. To this end, Hazard‑specific Stakeholder Interaction (HaSSI) groups coordinate collaborative work on windstorms, heavy precipitation, and heat/drought.
The HaSSI Wind group includes projects investigating physical mechanisms behind cyclones (CyclEx), applying newly developed statistical methods (CoDEx and SCaHA), analyzing storm-related impacts (COO, FORTEC and ECCES II), and developing methods for knowledge exchange (ClimXchange). With the pressure tendency equation, project CyclEx quantifies the influence of diabatic heating on cyclone intensification. Cyclones with a relatively large diabatic heating influence exhibit steeper deepening rates, more warm conveyor belt activity, increased precipitation, and stronger wind gusts compared to cyclones with a small diabatic influence. Project CoDEx develops statistical models to estimate extreme values in space and time. Project SCaHA focuses on statistical modeling of clustering and seasonality in vorticity extremes over the North Atlantic using the fractional compound Poisson process, and on identifying suitable models for different regions. As for the storm-related impacts, project COO uses a tracking methodology to assign a loss motivated storm severity index to each storm event and to perform a loss motivated ranking of historical and future events. Project FORTEC uses logistic regression models to identify relevant storm damage factors and model current and future storm damage risk for two damage types: vegetation damage along railway lines and building damage. Project ECCES II uses hydrodynamic tide-surge model and sensitivity experiments to evaluate the impact of regional sea level rise on storm surges in the North Sea with a focus on the nonlinear processes. With respect to stakeholder engagement, project ClimXchange strengthens climate research communication by providing hands-on guidance on suitable approaches, methods and communication skills. With joint efforts, we aim to provide a diverse view regarding storms in Europe and exchange actively with relevant stakeholders.
How to cite: Liu, X., Lorenz, R., Grieger, J., Ulbrich, U., Friederichs, P., Pinto, J. G., Christ, S., Fischer-Frenzel, P., Fried, R., Mendel, M., Merkes, S. T., Quinting, J., and Zimmermann, T.: ClimXtreme addressing (large scale winter) storm events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19825, https://doi.org/10.5194/egusphere-egu26-19825, 2026.
As climate change intensifies, shifts in temperature and precipitation extremes raise concerns about increasing river flood risks. Understanding and quantifying how climate change influences floods is relevant from both theoretical and practical perspectives. From a research standpoint, although flood trend detection has been widely investigated, attributing observed changes to specific drivers remains challenging. From a practical perspective, reliable methods are needed to incorporate evolving climate conditions into flood predictions—particularly in ungauged basins—since traditional flood frequency analysis assumes stationarity.
In this work, we present research conducted at Politecnico di Torino addressing these issues. The objectives are to investigate the relationship between floods and climate extremes at both local and large spatial scales, and to propose methods for linking changes in flood frequency curves to evolving precipitation and temperature patterns. The study area is the Great Alpine Region (GAR), including the Po River valley in Northern Italy. Both data-based and modelling approaches are employed.
The data-based approach relies on the derived flood frequency framework and estimates flood sensitivity to changes in precipitation extremes by linking flood frequency curves and Intensity–Duration–Frequency curves through quantile–quantile relationships at the local scale. The modelling approach is based on the application of a regionally distributed rainfall–runoff model forced by climate model outputs, allowing changes in flood event characteristics to be investigated at the regional scale.
This study is carried out within the Clim2FlEx project and the RETURN Extended Partnership and is funded by the Italian Ministry of Education, Universities and Research (MUR, PRIN project 2022AX3882) and the European Union NextGenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005 – Spoke VS1).
How to cite: Viglione, A., Lombardo, L., Cafiero, L., Basso, A., Mazzoglio, P., Ganora, D., Claps, P., and Laio, F.: Linking climate and rainfall extremes to flood changes: data-based and modelling approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19843, https://doi.org/10.5194/egusphere-egu26-19843, 2026.
Flooding remains one of India’s most persistent hydroclimatic threats, driven by monsoon variability, rapid urbanisation, and chronic gaps in observational hydrometric data. These limitations affect both fast-growing cities and large river basins, where ungauged tributaries and limited socio-economic preparedness compound the overall risk. To address this challenge, we develop a holistic, multi-scale flood-risk assessment framework that integrates reanalysis-driven flood hazards with socio-hydrological vulnerability patterns across India. First, four leading Hydrological Reanalysis Datasets, ERA5, IMDAA, CFSR/CFSv2, and MERRA-2, are evaluated using three decades of rainfall. ERA5 emerges as the most reliable dataset across diverse hydro-climatic zones. Validation using CC, KGE, NSE, POD, FAR, EB, and CSI shows strong agreement with gauged data. At the city scale, compound flood behaviour is quantified using bivariate and trivariate copulas linking long-duration rainfall (Rx1day), short cloudbursts (N25), and annual peak discharge (Q). River-connected cities exhibit pronounced upper-tail dependence under Gumbel–Hougaard structures, revealing synchronised extremes where intense rainfall and river overflow co-occur. Non-river cities show distinct rainfall-only compound signatures. These joint-probability structures provide realistic estimates of compound flood likelihoods in complex urban environments. At the basin scale, an extreme-value workflow using rolling annual maxima, KS-based distribution selection, and event-shape normalisation is applied to generate gridded 3-day, 5-day, and 7-day RP100 rainfall and discharge inputs. These time series force LISFLOOD-FP to produce representative design-flood hazard layers—depth, velocity, and depth×velocity—across India’s major basins. To capture the human dimension of flood impacts, a socio-hydrological module integrates 54 indicators of exposure, sensitivity, and adaptive capacity using a multi-criteria decision-making approach. District-level vulnerability scores and rankings reveal high- and very-high-vulnerability clusters across eastern, central, and northeastern India. These socio-economic patterns are analysed alongside hazard outputs, identifying critical “hotspot” regions where high exposure (population and area), weak coping capacity, and severe hydrodynamic hazards converge.
How to cite: Singh, H. and Mohanty, M. P.: Towards a Dual-Scale Flood Risk Assessment in India: Copula-Based Urban Extremes, Basin-Scale Design Flood Simulation, and Socio-Hydrological Vulnerability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1115, https://doi.org/10.5194/egusphere-egu26-1115, 2026.
In Central Europe, climate change contributes to an increasing frequency of devastating flood events, such as those observed in Western Europe in July 2021. While floods with return periods of up to 200 years have been relatively well studied, understanding and preparedness for more extreme, less frequent High-Impact-Low-Probability (HILP) floods remain limited. A key tool to assess the potential consequences of such events is the stress-test scenario. More specifically, these are hypothetical yet plausible simulations of a very low-likelihood flood events.
This review systematically analyses scientific studies that apply stress-testing approaches to HILP floods. The focus lies on research examining the impacts of very extreme pluvial and fluvial floods on humans, the built environment, and critical infrastructure. A systematic SCOPUS keyword search initially identified ~12,000 studies, which were reduced to 137 relevant publications using a filtering process assisted by a large language model. The selected studies are differentiated by how physical boundary conditions are derived, floods are modeled, and impacts are quantified.
The analysis shows that most studies use univariate statistical methods to derive hypothetical rainfall events, while more complex approaches such as climate model reforecasts or multivariate weather generators are employed far less frequently. A wide range of techniques is used to modify historical events to simulate unprecedented flooding. Counterfactual scenarios in flood modeling mainly investigate the effects of reservoirs and similar structures, whereas other simulations explore the potential of early-warning systems to reduce exposure. In terms of impact modeling, the reviewed literature examines a broad range of system components. About 60% of studies employ simple GIS overlays to assess the number of structures affected by floodwaters, while more advanced modeling tools include agent-based models, cascading impact models, network theory, and multi-criteria decision models. Only a few studies assess multi-sectoral impacts, and their analyses are often shallow or overly simplified. Future research should address this gap to achieve a more comprehensive understanding of the potential damages caused by HILP floods.
How to cite: Lennartz, M., Vorogushyn, S., and Merz, B.: Stress testing approaches for High-Impact-Low-Probability floods: A review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1846, https://doi.org/10.5194/egusphere-egu26-1846, 2026.
Climate change is expected to alter floods in complex ways. Understanding changes in flood frequency and driving processes under changing climatic conditions is urgently needed to develop sustainable adaptation measures to floods in rivers such as the Swiss section of the Rhône, which has been hit by a severe flood in June 2024. The investigation of future flood characteristics and driving processes is, however, challenging because these events exhibit large interannual variability and are, among other processes, caused by localized intense precipitation, which is not optimally represented in global climate model simulations. High-resolution initial condition large ensembles and convection-resolving climate simulations can be used to address these challenges. This work leverages these two types of climate ensembles and process-based hydrologic modeling to quantify long-term changes in peak flows in the Swiss section of the Rhône until the end of the century, and their uncertainty. We will use these simulations to investigate the driving processes underlying future floods and how these differ from those relevant for flood generation today. In doing so, we provide information crucial for decision-making related to future flood protection and adaptation.
How to cite: Elkouk, A., Astagneau, P. C., Wood, R. R., and Brunner, M. I.: Improving flood projections in the Rhône River using high-resolution initial condition large ensembles and convection-resolving climate simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4837, https://doi.org/10.5194/egusphere-egu26-4837, 2026.
The Spanish Mediterranean region is frequently impacted by flash floods, driven by intense convective rainfall, the presence of torrential basins, and dense urbanization in flood-prone areas. This situation can be aggravated by climate change, as demonstrated in recent studies. In this context, one way to reduce risk is to decrease vulnerability by improving both early warning systems and public preparedness. The recently completed Next Generation Flood2Now project aimed to address these two points through an interdisciplinary approach that brought together experts in hydrology, meteorology, sociology, databases, and complex systems, complemented by the participation of a private company. Two basins that suffer frequent flooding were chosen for the project: the torrential basin of the Francolí River, which can be dry in some reaches and times of the year but often experiences catastrophic flash floods as a result of intense rainfall, and the Arga River basin, characterized by a permanent flow produced by snowmelt and rainfall.
The ultimate goal of the project was to develop a hydrometeorological prediction chain that can be used operationally to aid decision-making in the face of potential floods. This was achieved using the INUNGAMA and PIRAGUA_flood (Llasat et al., 2024) databases, which contain all recorded floods in Catalonia and the Pyrenees, respectively, between 1980 and 2020. Based on these databases, an analogue model was developed (Guzzón et al., 2025) that considers the geopotential fields of 1000 and 500 hPa and weather types classified according to the method of Beck et al. (2007). In parallel, the RIBS hydrological model (Garrote and Bras, 1995) was calibrated at a set of points in the respective basins, where streamflow data recorded at gauging station are available, and subsequently fed with rainfall fields corresponding to flood events and their analogues, generating a probabilistic flow output that allows estimating the uncertainty of a given flood event causing damage (Carril-Rojas et al., 2025). For this purpose, flood compensation payments were taken into account, based on information from the Insurance Compensation Consortium. The data generated by the analogues, as well as the predictions obtained from the GFS, constituted the input for the Delft-FEWS platform (https://oss.deltares.nl/web/delft-fews/), which generates a set of flow rates and an exceedance warning as output. To update the flood databases, an AI-based methodology was created that extracts information from the press, analyzes it, and inputs it into the database. At the same time, citizen science campaigns, workshops and exhibitions have been developed, both to raise awareness among the population in both basins and to obtain more real-time observations of the river level. The contribution presented here shows the methodological synthesis of the project and the main results.
Carril-Rojas, et al., 2025. A Flood Forecasting Method in the Francolí River Basin (Spain) Using a Distributed Hydrological Model and an Analog-Based Precipitation Forecast. Hydrology, https://doi.org/10.3390/hydrology12080220
Garrote, L.; Bras, R.L., 1995. A Distributed Model for Real-Time Flood Forecasting Using Digital Elevation Models. JoH, https://doi.org/10.1016/0022-1694(94)02592-Y
Llasat, M.C., et al., 2024. Floods in the Pyrenees: A global view through a regional database, NHESS, https://doi.org/10.5194/nhess-24-3423-2024
How to cite: Llasat, M.-C., Marcos Matamoros, R., Guzzon, C., Arbaizar, J., Cabañas, A., Cachay Melly, J., Carril-Rojas, D., Díaz Guilera, A., Fernández-Fidalgo, J., Garrote, L., Llasat-Botija, M., Marinelli, D., Mediero, L., and Varela, O.: Improvement of early warning systems for flood risk with a distributed hydrological model and an analog-based precipitation forecast, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8029, https://doi.org/10.5194/egusphere-egu26-8029, 2026.
To assess flash flood susceptibility, an analysis of physiographic parameters of catchments affected by flash floods in the past was conducted. The study focuses on 17 catchments, ranging from 9 to 80 km², with water gauge stations in the Czech Republic. Based on parameters such as catchment area, slope, elevation, Melton ratio, river length and slope, river network length and density, shape index, arable land proportion, curve number, and road network density, categorization into clusters I–III was performed using principal component analysis and k-medoids clustering. To evaluate the hydrological response in relation to these parameters, the flashiness index (quantifying the magnitude and timing of the flood wave) was calculated for events in which peak discharge exceeded the 1-year return period discharge. The results show that the highest flashiness values were recorded in a group of small, steep catchments characterized by high terrain roughness, maximum elevations, a dense river network, and compact shape.
How to cite: Honzíčková, D., Šulc Michalková, M., Borga, M., Brázdil, R., Štěpánek, P., Zahradníček, P., Coufal, P., Geršlová, Z., and Caletka, M.: Why do some catchments exhibit a more flashy response? Catchment parameters controlling flash flood generation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11176, https://doi.org/10.5194/egusphere-egu26-11176, 2026.
Flooding is one of the most severe natural hazards worldwide, causing substantial economic losses and catastrophic impacts. Although levees are widely implemented for flood mitigation, few global flood models explicitly incorporate their influence on flood routing and risk assessment. In our previous study (Zhao et al., 2025, doi: https://doi.org/10.1029/2024WR039790), we developed a levee module for the CaMa-Flood model and generated levee parameters for global ungauged rivers. Furthermore, recent GPU-based computational optimizations in CaMa-Flood model (Kang et al., 2026, doi: 10.22541/essoar.176442648.85093032/v3) now enable simulations of global flood dynamics at high spatial resolution within a feasible timeframe.
These advancements allow us to analyze long-term changes in flood hazards and risks while explicitly accounting for levee effects. In this study, we forced the CaMa-Flood model with ensemble CMIP6 runoff data to simulate historical and future river hydrodynamic changes at the daily scale, comparing scenarios with and without levee considerations. Our results reveal that levees significantly mitigate global flood hazard. Driven by this protection, urbanization rates within levee-protected areas have substantially outpaced those in unprotected regions over the past four decades. Conversely, flood hazard in unprotected river reaches increases due to the hydrodynamic effects induced by levee construction. Regarding flood exposure, multi-model estimates show that while levees have historically played a crucial role in shielding a substantial fraction of the floodplain population and slowing the growth rate of flood exposure, rapid population growth has largely offset these protective benefits. Consequently, the absolute flood-affected population under levee protection in the present day remains at a historically high level, comparable to that of previous decades without levees. Furthermore, projections indicate that flood exposure will continue to increase through the mid-21st century under changing climate conditions. Overall, this work provides new insights into global flood modeling and risk assessment and supports improved flood management and decision-making in levee-protected regions.
How to cite: Zhao, G., Yamazaki, D., Hirabayashi, Y., Khanh, D. N., and Kang, S.: New Insights Into Global Flood Hazard and Risk Considering Levees Under a Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15543, https://doi.org/10.5194/egusphere-egu26-15543, 2026.
Climate change is expected to alter the frequency, magnitude, and temporal structure of flood waves, with direct implications for local scour development at bridge piers and, consequently, for bridge safety and management. While numerous studies have addressed individual methodological components of this problem - such as climate change projections, hydrological modelling, or scour estimation - the methodological links between climate change indicators, flood wave characteristics, and local scour processes remain fragmented and are often treated in isolation. This contribution presents preliminary insights from an ongoing systematic literature review that investigates how climate change signals are propagated through hydrological and hydraulic modelling chains and ultimately reflected in local scour assessments at bridge piers. The review focuses on peer-reviewed studies addressing (i) climate change modelling and approaches, (ii) hydrological representations of flood waves, including peak flow, volume, duration, and hydrograph shape, and (iii) deterministic and probabilistic methods for evaluating local scour. Attention is given to how uncertainties are treated across these methodological steps and to the extent to which flood wave characteristics beyond peak discharge are explicitly considered. The preliminary synthesis highlights recurring methodological patterns, key knowledge gaps, and inconsistencies in current practice, especially regarding the integration of climate projections with hydrological design events and scour modelling frameworks. The findings are organized into a structured classification of modelling approaches, input data requirements, and uncertainty treatment, providing a basis for further in-depth analysis. This contribution provides a structured synthesis of existing methodological approaches and identifying gaps relevant for future model development and application of climate change assessment on erosion processes around bridge piers.
Acknowledgment:
This work has been supported in part by the Croatian Science Foundation under the project SERIOUS – Synthetic dEsign hydrographs undeR current and future clImate for local bridge scOUr aSsessment (IP-2024-05-1497) and by the “Young Researchers’ Career Development Project – Training New Doctoral Students” (DOK-2020-01-5354).
How to cite: Potočki, K., Panchanathan, A., Lacko, M., and Bezak, N.: Climate Change, Flood Wave Characteristics and Local Scour: A review of Modelling Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16359, https://doi.org/10.5194/egusphere-egu26-16359, 2026.
Future fluvial flood risk can be estimated by applying change factors (CFs) to present day fluvial flood hydrology. At the global scale, CFs can be derived from the outputs of GCM-GHM ensemble pairs, such as ISIMIP3b. CFs are often calculated based on the proportional change of an index flood (e.g. median annual flood) between the future and present day, and subsequently applied over all return periods. Whilst this approach is statistically robust given the limited number of samples in ISIMIP3b, it assumes that rare and regular floods change proportionally. Scientific literature disputes this, suggesting that future changes may vary significantly based on flood rarity. Calculation of return period specific change factors can be achieved with stationary extreme value analysis on baseline and future periods, however the limited samples from climate ensembles result in noisy change factors for extreme events. Moreover, the analysis applied using different ensemble members results in greater uncertainty. A potential method to increase sample size to reduce noise is to apply non-stationary extreme value analysis across an ensemble’s entire time series.
This work compares multiple approaches to derive return period dependent CFs, including stationary flood frequency analysis, and non-stationary GAMLSS, across multiple distributions and ensemble members. We demonstrate and assess the differences in CFs between 2y (regular) and 100y (rare) floods, using Europe as a test domain. We find that CFs can be modeled as return period dependent, with clear spatial patterns present, and significant differences in changes in rare and regular floods in many locations. The variation of CF with return period depends on region, driving GCM - GHM pair, and the selected distribution fitting approach. In some locations, GCM-GHM pairs largely agree on the degree and direction of change between rare and regular floods, yet in others, GCMs-GHM pairs can give very different estimates of this (including opposing directions). Stationary flood frequency analysis for CFs results in greater noise, and CFs with greater magnitude. The GAMLSS approach significantly mitigates spatial noise, but reduces the intensity of changes in CFs as a function of RP. Our study overall highlights the importance of considering how CFs vary by return period, and how this variation itself is critically dependent on the driving GCM-GHM ensemble used.
How to cite: Hatchard, S. and Addor, N.: Do Small and Large River Floods Change Differently under Future Climate Change? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17320, https://doi.org/10.5194/egusphere-egu26-17320, 2026.
ABSTRACT
Compound drought–heat events are increasing under climate change, yet quantitative assessment of how antecedent drought alters extreme heat risk remains limited. This study examines the relationship between antecedent moisture conditions and extreme summer temperatures in Korea by combining correlation analysis with copula-based probabilistic modeling. Spearman’s rank correlation analysis reveals a consistent negative association between antecedent Standardized Precipitation Index (SPI) and daily maximum temperature (Tmax), indicating that drier conditions are systematically linked to higher summer temperatures. Copula models are then used to characterize the joint dependence between SPI and Tmax and to estimate conditional exceedance probabilities and return periods under different moisture states. The results show that dry and extremely dry conditions increase the probability of exceeding high Tmax thresholds and substantially shorten return periods, whereas wet conditions suppress extreme heat risk. The influence of antecedent drought becomes more pronounced for longer return periods, highlighting enhanced sensitivity in the extreme tail of the temperature distribution. These findings suggest that extreme heat risk is dynamically conditioned by prior hydrological states, emphasizing the importance of accounting for antecedent drought in interpreting and anticipating high-impact temperature extremes.
Acknowledgments
This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).
How to cite: Lee, J., Jung, H., Kim, H., and Kim, S.: Analysis of the Impact of Preceding Drought on Heatwave Extremes: Focusing on South Korea., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9019, https://doi.org/10.5194/egusphere-egu26-9019, 2026.
ClimXtreme is a research programme funded by the German Federal Ministry of Research, Technology and Space (BMFTR) that comprises 25 individual projects and aims to improve understanding of European extreme weather events and associated uncertainties under anthropogenic climate change.
As part of the cross‑project collaboration, a coordinated approach is being established to initiate and sustain targeted stakeholder communication and support it throughout the project period. The aim is to develop information, data and tools targeted to stakeholder needs. To this end, Hazard‑specific Stakeholder Interaction (HaSSi) groups coordinate collaborative work on windstorms, heavy precipitation, and heat/drought. This contribution summarizes activities within the HaSSi Heat/Drought group and provides an overview of the activities and outcomes within the project.
Our group brings together diverse projects that study heatwaves and droughts from multiple perspectives. These include their underlying large‑scale dynamics, their impacts (for example on crops and fire weather), and event‑based attribution methods. The combination of expertise from various fields gives us a multifaceted perspective on heat and drought events, which we use in two ways:
Communication with stakeholders is maintained through monthly online seminars, in which scientific findings and their relevance to stakeholders are discussed, thereby gathering valuable input from the stakeholders' perspectives. As valuable, cross-project output tailored to stakeholders, we also analyse current extreme events from the multifaceted view gained from the respective individual projects. We illustrate this approach using findings from the ClimXtreme report on the European Summer 2025.
How to cite: Lemburg, A., Szemkus, S., Buschow, S., Dietz, V., Dillerup, I., Feldmann, H., Fischer-Frenzel, P., Friederichs, P., Grieger, J., Kraulich, F., León-FonFay, D., Merkes, S. T., Pfleiderer, P., Pinto, J. G., Schröter, J., Sippel, S., Ulbrich, U., Vlachopoulos, O., and Zimmermann, T.: ClimXtreme addressing heat and drought events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19526, https://doi.org/10.5194/egusphere-egu26-19526, 2026.
Poland’s location in the mid-latitudes determines a moderate influx of solar radiation and characteristic circulation conditions that strongly control weather variability. Of particular importance is the dominance of westerly circulation, enhanced by the zonal configuration of relief. The frequent passage of cyclones and atmospheric fronts, together with the advection of air masses with highly contrasting thermal properties, results in pronounced weather variability. Depending on the direction of air-mass advection, both extremely warm and extremely cold conditions may occur.
Since the late 20th century, the climate has been characterized by the predominance of anomalously warm months, seasons, and years. Notable examples include the exceptionally hot summer of 2003 in Western Europe and the summer of 2010 in Eastern Europe, both of which caused thousands of excess deaths among populations unaccustomed to prolonged heat stress. At the same time, extreme cold events still occur. Although they have become less frequent and less intense than during the 20th century, they continue to generate severe economic and biometeorological impacts. An example is January 2017 in the Balkan Peninsula, which was among the coldest and snowiest months on record in that region.
In the current year, a pronounced negative temperature anomaly was also observed: in May, snowfall and widespread frost occurred over large parts of Poland, as widely reported by the media. The aim of this study was therefore to assess how strongly thermal conditions in May 2025 deviated from the long-term climatological mean in Poland. Specifically, the study seeks to quantify the magnitude of the air-temperature anomaly and to identify the synoptic conditions responsible for the persistence of anomalously low temperatures.
The primary dataset consists of mean monthly air temperatures for May for the period 1951–2025 from 60 synoptic stations in Poland. These publicly available data were obtained from the database of the Polish national meteorological service (IMGW-PIB, https://danepubliczne.imgw.pl/). This dataset was used to calculate the magnitude of the temperature anomaly in May 2025. Anomalies were expressed both in absolute terms (°C) and in standardized units (multiples of the standard deviation, SD). A second dataset, also obtained from IMGW-PIB, comprises daily mean, maximum, and minimum air temperatures for May for the 75-year period 1951–2025 from selected stations. Synoptic conditions were analysed using surface and upper-air weather charts from the Polish (meteo.imgw.pl) and German (www.wetter3.de) meteorological services.
Preliminary results indicate that the cold conditions over Poland were primarily controlled by low-pressure systems that induced advection of cold air from the northern sector. Their persistence was favoured by a characteristic omega-blocking pattern over Western Europe and the presence of a deep upper-level trough over Central Europe. This configuration effectively inhibited the eastward progression of baric systems, allowing the prevailing weather regime to persist over the region for an extended period.
How to cite: Guzik, I. and Twardosz, R.: Global warming vs local reality: Poland's exceptionally cool May 2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13998, https://doi.org/10.5194/egusphere-egu26-13998, 2026.
Climate change is altering the frequency, intensity, and spatial patterns of extreme weather events. Urban areas are particularly affected because population, critical infrastructure, and economic assets are concentrated in a relatively small area, which increases the potential impacts of extreme events. Climate risk depends not only on the hazard itself, but also on what is exposed and how sensitive it is to the damage. Understanding climate risk requires considering how hazards interact with exposure and vulnerability. For this reason, risk-based approaches are increasingly needed to support climate adaptation and decision-making. The increasing availability of open-source tools, including the CLIMADA model, has made climate risk assessment more accessible. This study applies the CLIMADA risk assessment framework to evaluate urban heat-stress risk in Vilnius, Lithuania.
The analysis focuses on heat stress as an illustrative case to examine potential impacts on human health under three levels of global warming: the recent past, +2 °C, and +4 °C relative to pre-industrial conditions. Climate hazards are characterised using projections from climate models, and uncertainty in future conditions is represented through an ensemble of simulations. This ensemble consists of five CMIP6 models: CanESM5, ACCESS CM2, GFDL-CM4, MPI-ESM1-2-LR, and NorESM2-MM. Heat-stress intensity affecting people is quantified using the Humidex index, which is calculated from gridded air temperature and relative humidity data and used as the hazard intensity in the risk assessment. Exposure is defined as the spatial distribution of people and relevant assets. Because this study focuses on heat-stress risks to human health in urban areas, exposure is characterised using a set of socio-demographic and urban indicators, including population distribution, age structure, the locations of critical infrastructure, and urban surface characteristics. Vulnerability describes how sensitive exposed people or assets are to the hazard and is represented through vulnerability functions. In this analysis, vulnerability is expressed through the mean damage degree and the percentage of affected assets, which are used together to estimate the mean damage ratio.
Preliminary results are presented as risk maps showing the spatial distribution of heat-related health risk in Vilnius under different climate change scenarios. This spatial information supports the identification of priority areas for adaptation planning and risk reduction.
How to cite: Kapilovaite, J.: Assessing Urban Heat-Stress Risk Under Future Climate Scenarios: A Case Study of Vilnius, Lithuania, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15466, https://doi.org/10.5194/egusphere-egu26-15466, 2026.
This paper presents a comparison of three methods for determining cumulonimbus (Cb) cloud-top heights. Two methods are prognostic, based on the 1/4 CAPE and 1/2 CAPE approaches, while the third is a diagnostic polynomial method. The analysis was conducted for five convective events that developed under different synoptic and mesoscale conditions. Surface and upper-air synoptic charts, WRF simulations, radar, and radiosonde data, as well as observed lightning data are used to characterize the convective environments and evaluate the methods. The polynomial method, which approximates moist adiabats using a fifth- degree polynomial function, proved to be successful in diagnosing the Cb cloud-top heights at the observed locations. Therefore, cloud-top heights obtained from the polynomial method are used as a reference values for assessing the performance of the prognostic methods. Results show that, for most locations and events, both CAPE-based methods tend to overestimate Cb cloud-top heights. The 1/4 CAPE method generally exhibits smaller deviations from the observed values of Cb cloud-top heights than the 1/2 CAPE method. Nevertheless, the Cb cloud-top heights estimated using both CAPE methods exhibit a strong linear correlation with observed heights in four out of five analyzed events.
How to cite: Oštrić, S., Šoljan, V., Babić, K., and Telišman Prtenjak, M.: Comparison of Methods for EstimatingThunderstorm Cloud-Top Heights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20647, https://doi.org/10.5194/egusphere-egu26-20647, 2026.
Hailstorms are a major source of insured losses in Europe, with unprecedented damage in France in 2022 exceeding €6 billion. Anticipating how hail frequency may evolve under climate change is therefore critical for insurance risk management. This study assesses future changes in atmospheric environments conducive to hail using EURO‑CORDEX regional projections based on CMIP5 simulations. Sixteen GCM–RCM pairs provide 6‑hourly atmospheric fields at 11 km resolution, enabling a temporal and spatial high‑resolution, multi‑model analysis. Following the approach of Rädler et al. (2018), we compute key convective indices - Lifted Index (LI), 0-6 km vertical wind shear, and accumulated precipitation - and flag hail‑conducive days as a proxy for hail occurrence. Over the historical baseline (1980–2005), France experienced approximately 75 hail-conducive days per season (April–October). By mid-century (2050), the multi-model mean projects ~95 days, corresponding to a ~26% increase, rising to ~40% by 2100 under RCP8.5. The upward trend is statistically significant; however, substantial spatial heterogeneity is observed across France and adjacent countries. These findings have direct implications for the insurance sector: increasing hail risk challenges current pricing models, portfolio management, reinsurance treaty, and underscores the necessity of integrating climate projections into catastrophe modeling frameworks.
Keywords: hailstorms; severe convective storms; EURO‑CORDEX; CMIP5; RCP8.5; Lifted Index; vertical wind shear (0–6 km); multi‑model ensemble; regional climate modeling; France; insurance risk.
How to cite: Romain, M., Gilles, A., and Blandine, L.: Hailstorms in a warming climate: What future for France and insurance sector?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6709, https://doi.org/10.5194/egusphere-egu26-6709, 2026.
An increasing frequency of severe weather events, including damaging winds and tornadoes, poses significant hazards to infrastructure, ecosystems, and society (Fischer et al., 2025). Accurate and rapid surveys of damage caused by severe convective wind events remain challenging, yet they are essential for understanding impacts, refining wind-damage classification schemes, and improving severe weather databases. This study demonstrates an integrated workflow that combines a GIS-based mobile field-mapping application with multi-scale remote sensing data (UAV imagery and high-resolution satellite observations) to document and assess severe wind damage using the new International Fujita Scale (ESSL 2023). The approach is demonstrated through a case study of a tornado and associated severe wind damage near Hažlín in eastern Slovakia. A configurable mobile mapping platform tailored for damage surveys was used by the field team to collect standardized, georeferenced damage observations, damage type and intensity, and photographic documentation. Complementing the field mapping, high-resolution UAV surveys and pre-/post-event satellite imagery supported detailed characterization of vegetation and structural damage patterns at spatial scales unattainable from ground surveys alone. By integrating standardized field observations with remote sensing, the proposed approach improves damage classification accuracy and contributes to severe-wind climatological databases.
ESSL: International Fujita (IF) Scale, https://www.essl.org/cms/research-projects/international-fujita-scale/ (last access: 12 January 2025), 2023.
Fischer, J., Groenemeijer, P., Holzer, A., Feldmann, M., Schröer, K., Battaglioli, F., Schielicke, L., Púčik, T., Antonescu, B., Gatzen, C., and TIM Partners: Invited perspectives: Thunderstorm intensification from mountains to plains, Nat. Hazards Earth Syst. Sci., 25, 2629–2656, https://doi.org/10.5194/nhess-25-2629-2025, 2025.
How to cite: Fedor, T., Nováková, M., and Onačillová, K.: Damage assessment of severe wind events using mobile field mapping and remote sensing: A case study of the Hažlín tornado, Slovakia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11984, https://doi.org/10.5194/egusphere-egu26-11984, 2026.
Windstorm events are among the most damaging weather-related hazards in Europe, accounting for approximately 70% of the total insured losses. Existing research has primarily focused on storm climatology, hazard metrics, and loss-based assessments derived from insurance or damage data. While these approaches have advanced our understanding of windstorm dynamics and impacts, they provide limited insights into the spatial and quantitative characteristics of exposure.
Windstorm damage occurs where localised extreme wind gusts intersect with exposed socio-environmental assets. Exposure is therefore a central, yet still insufficient quantified, component of windstorm risk. In practice, exposure is often approximated through loss proxies or simplified indicators, largely due to persistent challenges in defining spatially coherent storm-affected areas from gust observations at the event scale.
This study develops an event-based windstorm exposure analysis for Sweden by linking observationally identified windstorm events with high-resolution national exposure datasets. Windstorms are identified from long-term station observations using local percentile exceedances combined with spatio-temporal clustering, resulting in a catalogue of extreme events. Storm-affected areas are identified using a single exceedance-based footprint approach applied to two wind data sources: interpolated station observations and ERA5 reanalysis. In both cases, footprints are defined using locally derived percentile thresholds of wind gust intensity, capturing areas that experienced unusually strong winds relative to local conditions.
For each event and footprint definition, exposure is quantified through the spatial intersection of extreme winds with datasets that describe population distribution, buildings, transportation, forest structure, and topography. Exposure metrics include population counts, infrastructure length, building numbers, and forest area.
A comparative analysis of exposure estimates across different footprint methodologies is conducted to assess the sensitivity of windstorm exposure to footprint definition. The resulting dataset provides an event-based representation of windstorm exposure in Sweden and establishes a foundation for improved attribution of impacts and future vulnerability and risk analyses.
How to cite: Georgali, E. and Karagiorgos, K.: Event-based windstorm exposure in Sweden using observational and reanalysis-derived storm footprints., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20762, https://doi.org/10.5194/egusphere-egu26-20762, 2026.
