This study investigates the relation between Euro-Atlantic large-scale atmospheric circulation and extreme precipitation events (EPEs) in the Great Alpine Region (GAR). We analyze the connection between weather regimes (WRs)—recurrent and quasi-stationary circulation patterns—and EPEs to assess temporal and spatial variations.

The analysis covers the period 1940–2023, using daily geopotential height data at 500 hPa and daily total precipitation data from ERA5 reanalysis. WRs classification mainly follows the methodology outlined by Grams et al. (2017), enabling year-round characterization of atmospheric patterns, which are then linked to average precipitation and EPEs, defined as precipitation exceeding the 95th percentile of the distribution and an intensity greater than 15 mm/day (Q95R15).

Our results show diversities in the average precipitation patterns over the GAR when different regimes occur. In particular, Scandinavian Trough (ScTr), Greenland Blocking (GrBL), Scandinavian Blocking (ScBL) and Atlantic Ridge (AR) seem mostly connected with average precipitation, whose intensity varies according to the season.

Relating WRs and extreme precipitation, we observe that spatially the association between WRs and EPEs varies across GAR sub-regions and depends on the season. We detect higher frequencies of occurrence for ScTr, GrBL, ScBL, AR and Atlantic Trough (ATr) when precipitation above Q95R15 occurs. For instance, during autumn (SON), EPEs are primarily linked to ScTr, ScBL and AR regimes; during winter (DJF) we observe ScTr, GrBL, ScBL, AR and ATr instead. During spring (MAM) and summer (JJA) a clear association is elusive up to now, needing further analysis to be clarified.

Investigations into different sub-periods are ongoing, in order to obtain more insights about how decadal changes due to forced and/or internal variability in the Euro-Atlantic circulation affect the occurrence of EPEs in the GAR.

How to cite: Tessari, I., Giuntoli, I., and Corti, S.: Weather Regimes and Extreme Precipitation in the Great Alpine Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4110, https://doi.org/10.5194/egusphere-egu25-4110, 2025.

Given the current warming trend of our climate system, the frequency and intensity of extreme weather events are expected to have a significant impact on flood dynamics. The Clim2FlEx project aims in this evolving context to assess how floods of different natures are linked to climate extremes under potential future climate scenarios.

This work focuses on the European Alps, an optimal natural laboratory for this topic due to the complex hydro-meteorological processes occurring in the region and its unique position at the intersection of the Mediterranean and continental Europe.

The methodology uses an innovative and integrated version of the TUWmodel, combined with a machine-learning-based regionalization approach, HydroPASS. Once the regional model is validated, it will enable hydrological runoff predictions for both current and future scenarios across the Greater Alpine Region. Based on these simulations, we aim to identify flood events in time and space, linking them to climate extreme indices and, ultimately, to the large-scale climatic phenomena driving their dynamics.

At the EGU, we will present the results obtained regarding the performance of the regional model, along with the steps taken, and those planned, for developing the spatio-temporal event detection strategies.

How to cite: Basso, A., Lombardo, L., and Viglione, A.: A distributed rainfall-runoff model to explore the connection between floods and climate extremes in the European Alps, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15233, https://doi.org/10.5194/egusphere-egu25-15233, 2025.

The link between climate extremes and river floods is complex and greatly affected by regional characteristics. River discharges are highly dependent on elevation and size of catchment in mountainous regions. This study explores the effects of orography on the precipitation-discharge relationship in the Greater Alpine Region (GAR). We make use of  daily discharge data and several reanalysis and observation datasets. The region is stratified into low (LE), and high (HE) elevation categories to assess variations in discharge responses. The correlation of discharges with precipitation at HE shows stronger relationship during the autumn season (September-November), while LE exhibits a stronger association in summer (June-August). Coarser resolution (>0.25^o) datasets show degradation of the association of precipitation with river discharge at both elevation categories,  although with a larger sensitivity of HE  to decreasing spatial resolution (i.e. 0.10^o to 1^o degree) as compared to the LE category. Significant sensitivity to spatio-temporal scales is found also in the intensity and duration of the climate extremes (ETCCDI indices) and their relationship with discharges in the GAR. This study emphasizes the advantages of high-resolution, multi-scale approaches to understand the intensity and duration of climate extremes and their impacts on river discharges. An improved framework integrating climate and orographic indices is essential to identify the complex relationships governing flood extremes in the GAR. The improved framework will contribute to the development of diagnostic tools and enhance the skill of future flood extreme projections by climate models.

How to cite: Kushwaha, V. K., Lombardo, L., Basso, A., Viglione, A., and Arnone, E.: Elevation dependent effects of precipitation on river discharge at different spatio-temporal scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-973, https://doi.org/10.5194/egusphere-egu25-973, 2025.

In this study, the variations of rainfall and river discharges were analyzed in the Chenyulan watershed in Nantou County, central Taiwan. The hydrological data, including rainfall, daily discharges and yearly maximum instantaneous discharge, were collected from the Neimaopu hydrology station for the period from 1972 to 2022, covering approximately 50 years. According to the data analysis, when the rainfall exceeds the average, the river discharges in the Chenyoulan catchment increases, with larger rainfall events leading to more significant changes. Upon comparing the long-term data, it was found that the maximum instantaneous discharge occurred on August 1, 1996, during the Herb Typhoon. Though this event did not coincide with the historical maximum for total rainfall, rainfall intensity or average rainfall intensity, it resulted in the maximum instantaneous discharge.

 All of the rainfall events, daily average discharge and yearly maximum instantaneous discharge are preliminarily analyzed as follows: 1. Rainfall in the catchment shows a positive correlation with river discharge; 2. The increase in rainfall characteristics in the catchment and the increase in discharge are not linearly related; 3. The non-linear reasons for the relationship between rainfall and maximum instantaneous discharge are preliminarily summarized as being related to soil conditions, different rainfall intensity locations and the runoff coefficients of various catchment units; 4. This study will subsequently estimate the average runoff coefficient of the catchment based on the relationship between individual rainfall and discharge, and conclude rational formula.

How to cite: Huang, W.-S., Chen, J.-C., Chien, K.-H., Lai, X.-Z., and Lai, Y.-T.: Impacts of rainfall variability on river discharges characteristics : A Case Study in Chenyulan Watershed, Taiwan, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10340, https://doi.org/10.5194/egusphere-egu25-10340, 2025.

Floods induced by rainstorm events (RSEs) are among the most frequent natural disasters and have a significant impact on ecosystems and human society. While most extensive researches have investigated the magnitude, frequency, and risk of floods, understanding the spatiotemporal evolution of contiguous flood-causing rainstorm events remains largely unexplored in China. Here, we collected historical flood disaster data from the Statistical Yearbook, news reports, and government sources and examined the evolution patterns of spatiotemporally contiguous flood-causing RSEs across China from 2000 to 2020, utilizing the connected component three-dimensional algorithm. Our results indicate that floods mostly occur in southern China (SC), followed by northern China (NC), with less frequency in northwestern China (NWC) and the Qinghai-Tibetan Plateau (TP). The flood-causing RSEs tend to occur with longer durations and higher magnitudes in SC and NC, while in NWC and TP, they are primarily characterized by short-term precipitation processes with lower magnitudes. Moreover, the flood-causing RSEs exhibit distinct evolutionary patterns in different subregions. In NWC and TP, RSEs generally move eastward and southeastward, with relatively longer lifespans, traveling longer distances at faster moving speeds, but covering smaller areal extent and lower accumulated rainfall amounts. In contrast, in both SC and NC, flood-causing rainstorm events are mainly moved in two directions, namely westwards and eastwards. These events have shorter average lifespans, and travel shorter moving distances at slower moving speeds, but have a larger areal extent and huge accumulated rainfall amounts. Our findings significantly enhance our understanding of flood-causing rainstorm characteristics in China.

How to cite: Wang, J. and Guan, X.: Spatiotemporal evolution patterns of flood-causing rainstorm events in China from a 3D perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7964, https://doi.org/10.5194/egusphere-egu25-7964, 2025.

How to cite: Pandidurai, D., Raghuvanshi, A. S., and Agarwal, A.: Atmospheric moisture linkages to flood inducing Multiday extreme precipitation in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3278, https://doi.org/10.5194/egusphere-egu25-3278, 2025.

Floods are one of India’s most catastrophic natural disasters, causing extensive loss of life and property. Recent research highlights that compound floods—arising from the interplay of multiple drivers—pose greater risks than individual flood events. Although compound flood drivers like precipitation and storm surge, precipitation and runoff, and others have been the focus of recent research globally, very limited research has been done on these flood drivers in India. To address this gap, we conducted a comprehensive compound flood analysis of Peninsular India river basins from 1980 to 2023, utilizing precipitation, runoff, and soil moisture data. Extreme events were identified using a certain percentile threshold (95^th and 99^th percentiles) for all the parameters and each parameter was initially subjected to a univariate analysis. The preliminary results indicate that individual drivers provide limited insights of these flood drivers. To address this, we employed a bivariate copula-based approach to estimate joint distributions at varying percentiles (25th, 50th, 75th, 90th, and 95^th percentile). The analysis using copula was focused to determine of exceedance probability, conditional probability, joint return period, and conditional return period for the paired variables: precipitation-runoff, precipitation-soil moisture, and runoff-soil moisture pairs, respectively. Our results illustrate that, especially in instances where there are multiple contributing components, bivariate analyses provide deeper insights into comprehending the complexity of flood dynamics. Additionally, it has been observed that some regions in our research region had shorter return durations and higher exceedance probabilities, suggesting that compound flood events of lower severity occur frequently. Identical patterns were noted for conditional return durations and conditional probabilities. These results underscore the critical importance of understanding the interconnections among flood drivers for effective flood risk estimation. Our study provides valuable insights for enhancing India’s flood management strategies by identifying disaster-prone regions and informing policymakers in the development of targeted mitigation measures.

How to cite: Mukherjee, A., Poonia, V., and Swarnkar, S.: Probabilistic Evaluation of Compound Flooding in Peninsular India: A Copula-Based Analysis of Precipitation, Runoff, and Soil Moisture , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-386, https://doi.org/10.5194/egusphere-egu25-386, 2025.

Flood event attribution, including the analysis of extreme precipitation and flood peaks, is crucial for understanding how anthropogenic climate change influences these events. This study employs an unconditional attribution approach to quantify changes in the likelihood of the July 2021 flood in the Ahr region, western Germany, in a factual world representing the current climate compared to a pre-industrial counterfactual world without anthropogenic greenhouse gas emissions.

To achieve this, the non-stationary weather generator nsRWG, conditioned on large-scale circulation patterns (CPs) and regional mean daily temperature (t2m), is used to generate 100 realizations of synthetic precipitation and temperature data over a 30-year period for both worlds. The CPs, derived from the classification of mean sea level pressure, and t2m are obtained from the ERA5 reanalysis dataset for the factual world and from natural historic simulations of several CMIP6 GCMs for the counterfactual world. The nsRWG-generated data are further disaggregated to an hourly resolution and fed into the hydrological model mHM, set up for the Ahr basin, to simulate streamflow and derive hourly peak flow. The simulated extreme precipitation and peak flows are analyzed to estimate the likelihood of the July 2021 flood event in each climate state, forming the basis for calculating the probability ratio between the two worlds.

Our model-based results indicate that the likelihood of 1-day and 2-day extreme precipitation of the Ahr event is on average 1.28 and 1.63 times higher, respectively, in the current climate. The flood peak appears to be 1.07 times more likely in the present climate compared to the counterfactual world. These findings suggest that anthropogenic climate change has notably increased the likelihood of events like the July 2021 flood. The use of a weather generator in combination with a hydrological model paves the way towards hydrologic event attribution and sets the stage for further research into attribution of flood impacts.

How to cite: Nguyen, V. D., Merz, B., Han, L., Apel, H., Guan, X., Kreibich, H., and Vorogushyn, S.: Attribution of the July 2021 flood event in the Ahr region to anthropogenic climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6938, https://doi.org/10.5194/egusphere-egu25-6938, 2025.

Out of all weather-related hazards, flooding has the most widespread impact globally, and the province of Quebec is no exception. In the past decades, dozens of riverside municipalities have felt the socio-economic consequences of flooding firsthand. Most of Quebec is characterised by a cold and humid continental climate, with precipitation year-round. Here, river flooding often takes place in the spring, due to snowmelt. Although important, snowmelt alone is not the only factor influencing flooding in the mid-latitudes. By bringing heavier than normal precipitation with them, extratropical cyclones are also known to be key contributors. The relationship between extratropical cyclones and flooding have been extensively studied on the West Coast of North America, but remains largely unexplored in eastern Canada. Thus, this study aims to link flooding events that have happened in the past 30 years in Quebec to their triggering extratropical cyclones and identify possible characteristics (genesis locations, trajectories, lifetime, progression speed, or precipitation intensity) that set these systems apart. Coupled with financial aid claims data, highlighting the differences between regular vs flood-inducing extratropical cyclones coming through Quebec can help describe the region’s flooding history and better prepare for future events. We also explore the involvement of atmospheric rivers in these extreme events. This analysis is performed using three databases. First, the Quebec Floods Financial Aid Claims Database provides the 14360 financial aid claims filed by individuals or businesses for material loss following flooding, from 1990-2022. Each claim contains the location of the damaged infrastructures, watershed involved, and closest river section. Second, the North American Extratropical Cyclone Catalogue provides extratropical cyclone tracks derived from the ERA5 reanalysis, available every hour from 1979-2020, and includes variables of interest such as precipitation and near surface wind-speeds. Third, the Global Atmospheric River Scale Database gives the occurrence and scale (based on integrated water vapor transport and duration of event) of atmospheric rivers every 6 hours from 1979-2020. By grouping the financial aid claims by location and date, 385 events were identified. Through this analysis, 550 extratropical cyclones (storms) of interest were identified and ranked according to their associated percentage of cumulated rain during the event. Five zones of storm genesis locations were identified: western Canada, Great Lakes and Ontario, US Northern East Coast and Quebec, Central US, and US East Coast. The genesis location of weaker storms was uniformly distributed among the five regions. However, most of the remaining 108 more intense storms were coming from two genesis locations: Central US (48%), and US East Coast (25%). For these two genesis zones, trajectories of stronger storms were found to be different from those of weaker storms. For example, tracks were more likely to move over land going up the US East Coast and go over the Great Lakes when coming from Central US. As for atmospheric rivers, their involvement in flood-events was found to be very high in the winter, and minimal in the summer. The combination of data used in this method offers new insights for investigating flooding events.

How to cite: Gagnon, C., Nadeau, D., Di Luca, A., and Anctil, F.: The role of extratropical cyclones in flooding in Quebec, Canada, from 1990-2020, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12781, https://doi.org/10.5194/egusphere-egu25-12781, 2025.

Convection-permitting regional climate models (CPRCMs) are increasingly recognized for their ability to improve extreme precipitation predictions, yet their application to hydrological modeling in complex terrains remains uncertain. This study evaluates the performance of CPRCMs in predicting hydrological extremes in two basins in Western Norway: Røykenes, dominated by rainfall-induced floods, and Bulken, characterized by snowmelt-induced floods. We compare the capabilities of a high-resolution convection-permitting model (HCLIM3, 3 km resolution) with a coarser regional climate model (HCLIM12, 12 km resolution) in driving two hydrological models: the physically based Weather Research and Forecasting Model Hydrological system (WRF-Hydro) and the conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model. Performance was evaluated based on precipitation, temperature, runoff, and hydrological extremes. We found that HCLIM3 exhibited significantly better performance in estimating annual maximum 1-day (Rx1d) and 1-hour (Rx1h) precipitation, with reduced biases compared to HCLIM12. It also showed added value in capturing the probability density distribution of daily and hourly precipitation, as quantified by the Distribution Added Value (DAV) metric. However, both HCLIM3 and HCLIM12 displayed cold biases, especially in mountainous areas. Besides, in the rainfall-dominated Røykenes basin, WRF-Hydro outperformed HBV in simulating extreme flood magnitudes across return periods (5, 10, 20, and 50 years). However, in the snowmelt-dominated Bulken basin, cold biases in HCLIM3 and HCLIM12 introduced uncertainties in snowmelt timing, leading to larger errors. The added value of HCLIM3 was observed in hourly discharge in the Røykenes basin. However, this benefit was less pronounced in the snowmelt-dominated Bulken basin, where temperature sensitivities significantly influenced snowmelt processes. Biases in HCLIM3 and HCLIM12 meteorological forcing propagated through hydrological models, leading to discharge errors, as highlighted by DAV metrics. This research highlights the importance of applying bias correction to CPRCM simulations to improve hydrological modeling of extreme events, especially in mountainous terrains where biases in temperature and precipitation critically affect hydrological processes.

How to cite: Li, L. and Xie, K.: Evaluating the added value of convection-permitting regional climate models in simulating hydrological extremes over basins in western Norway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13633, https://doi.org/10.5194/egusphere-egu25-13633, 2025.

Convective precipitation plays a crucial role in extreme weather events, significantly influencing regional hydrological patterns, especially in topographically complex areas such as the Greater Alpine Region (GAR). Despite its importance, the study of convective precipitation remains limited due to its high spatial and temporal variability, which poses challenges for accurate observation and representation in climate models. Reanalysis datasets, such as ERA5, offer a valuable resource for overcoming these challenges, providing consistent, high-resolution data derived from both observational records and model outputs. However, the convective component of precipitation in ERA5 remains insufficiently explored, particularly regarding extreme events and seasonal trends. This study investigates the convective component of precipitation in the GAR using the ERA5 reanalysis dataset, focusing on extreme precipitation and their seasonality. By applying extreme precipitation indices from the ETCCDI framework, we identify a significant increase in the convective fraction of precipitation in recent decades, particularly during summer extreme events, along with an extension of the summer convective season. Trends in monthly precipitation are found to be largely driven by changes in the convective component, emphasising its growing influence on regional precipitation patterns. Additionally, the study is extended to CMIP6 global climate models, providing further insight into the representation of convective precipitation in climate projections. This work contributes to advancing the understanding of convective processes in climate models, emphasizing a critical gap in the current representation of precipitation in mountainous regions.

How to cite: Saglietto, G. and Ferguglia, O.: Seasonality change in ERA5 convective precipitation in the Greater Alpine Region., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-847, https://doi.org/10.5194/egusphere-egu25-847, 2025.

One of the major challenges in hydrological research for estimating design flood events is accounting for the influence of climate change. These changes are reflected in increasingly frequent and intense fluctuations in river water regimes and sediment transport, indirectly affecting riverbed erosion processes. Therefore, assessing the long-term impacts on the lifespan of hydraulic structures (e.g., bridges) is crucial, requiring a comprehensive analysis of the interrelationship between climate change indicators, flood wave characteristics (including peak flow and hydrograph shape), and local riverbed erosion.

The SERIOUS project (Synthetic dEsign hydrographs undeR current and future clImate for local bridge scOUr aSsessment) aims to methodologically link synthetic design hydrographs (SDH) derived from statistical bivariate analysis under current and projected future climate conditions in the continental parts of the Danube River basin to the assessment of climate change impacts on bridge scour at selected pilot sites. The project objectives are to: (1) establish a methodological framework for determining control SDH based on literature reviews and available data in selected pilot areas; (2) apply and improve supervised and/or unsupervised machine learning algorithms to categorize different SDH types based on their shapes and/or topologies; (3) calibrate a regional hydrological model to evaluate climate change projections using historical discharge and water level data from the selected pilot areas; (4) investigate changes in SDH under climate change projections; and (5) develop a methodological framework for evaluating climate change impacts on bridge scour depth. These objectives are supported by the IAHS "Helping Decade" initiative (Working Group 11.1). The proposed project is expected to improve methodologies for determining SDH, serving as critical inputs for designing various engineering structures.

Acknowledgment:

This work has been supported in part by the Croatian Science Foundation under the project SERIOUS (IP-2024-05-1497) and the “Young Researchers’ Career Development Project – Training New Doctoral Students” (DOK-2020-01-5354).

How to cite: Potočki, K., Bekić, D., Bezak, N., Conradt, T., Pintar, D., Šrajbek, M., and Lacko, M.: Synthetic Design Hydrographs Under Current and Future Climate for Local Bridge Scour Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16468, https://doi.org/10.5194/egusphere-egu25-16468, 2025.

Ice-jam flooding linked with the interactions of hydrological and cryosphere processes is a serious threat to riverine communities in cold regions. This work uses the coupling of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrological model, which represents a wide range of hydrological processes, and the mizuRoute river routing model with that of a river ice model (i.e., RIVICE model) to project of ice-jam floods under changing climatic conditions. In fact, SUMMA and mizuRoute simulate streamflow, which is then passed to RIVICE to model ice formation and dynamics. The dynamics of streamflow simulated by SUMMA / mizuRoute include comprehensive representation of various hydrological processes, while the RIVICE model considers the processes of ice formation, frazil ice dynamics, and accumulation. This coupled modeling framework is applied to the Klondike River in Yukon, Canada, one of the regions historically affected by ice-jam flooding. This study uniquely integrates these models to enable projection of future ice-jam flood scenarios. The simulations are driven by climate projections from the CMIP6 datasets, enabling comprehensive assessments of future freeze-up events and associated flood risks at high spatial and temporal resolution. This work contributes to the increasing value of integrated hydrological and cryospheric modeling, improving flood risk assessments and informing adaptive strategies, such as improved forecasting systems and infrastructure design, for community protection in cold regions.

How to cite: Lindenschmidt, K.-E., Ghoreishi, M., and Eythorsson, D.: Modeling of Ice-jam Flooding: Integrating SUMMA with River Ice Processes for Climate Change Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20525, https://doi.org/10.5194/egusphere-egu25-20525, 2025.

In recent years, extreme runoff has been affected by increasing climate change, which causes non-stationary behaviors in extreme runoff series. Climate change is driven by external forcing and internal variability. However, the role of these two factors in runoff variability remains unclear. Taking the historical period as the baseline, this study employs four Single-Model Initial-Condition Large Ensembles (SMILEs) to investigate future changes in extreme runoff represented by annual maximum 1-day runoff (AM1R) over China and to evaluate the impacts of external forcing and internal variability on these changes. A decomposition-based non-stationary frequency analysis method is proposed to estimate the frequency changes of extreme runoff events, which incorporates components of runoff influenced by external forcing and internal variability. Two shared socioeconomic pathways (i.e., SSP2-4.5 and SSP5-8.5) are selected for the future. The results show that the catchments with increased AM1R are more than those with decreased AM1R under SSP-2.4.5 and SSP5-8.5 scenarios for all SMILEs, with the catchments showing decreased AM1R mainly in Qinghai-Tibet Plateau and northeastern China. The impact of external forcing on runoff is stronger than that of internal variability at more than 35% and 62% of catchments for all SMILEs under SSP2-4.5 and SSP5-8.5 scenarios, respectively. The catchments with significant trends of AM1R are mainly in the eastern Qinghai-Tibet Plateau under the SSP2-4.5 scenario, while those are mainly in Qinghai-Tibet Plateau and southwestern China under the SSP5-8.5 scenario. For changes in the frequency of extreme runoff events, corresponding to the 50-yr return level of AM1R in the historical period, the return period is projected to become shorter in at least 66% of catchments for all SMILEs under the two scenarios. The study indicates that extreme runoff events are likely to become more frequent in the future, which is important for the flood prevention policy.

How to cite: Liu, Y. and Chen, J.: Extreme runoff variation and non-stationary frequency analysis based on external forcing and internal variability decomposition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2720, https://doi.org/10.5194/egusphere-egu25-2720, 2025.

Floods are an ever-present risk to society and economy in Europe, influenced by both climatic and socioeconomic drivers. An accurate and timely attribution of impacts is important for risk management, “loss and damage” debate and public communication in context of climate change. Here, we discuss the opportunities and challenges of operationalizing attribution for European flood impacts in the framework of Horizon Europe project “Compound extremes attribution of climate change: towards an operational service” (COMPASS). The prospective operational service would build upon the framework for attribution of historical flood impacts for 42 European countries. The work so far includes an extensive modelling chain covering both riverine and coastal floods that can reconstruct temporal changes in hazard, exposure and vulnerability to quantify their influence on the observed flood impacts. It considers drivers such as climate change, catchment alteration, population and economic growth, land use change, and evolution of flood precaution and adaptation. High-resolution datasets with long time series are used to first reconstruct each flood event under the factual (historical) scenario, and then under counterfactual scenarios in which a particular climatic or socioeconomic driver is set to 1950 conditions. In this way, the role of each driver can be quantified relative to a common temporal benchmark. In total, 1729 impactful floods occurring between 1950 and 2020 were attributed to the various drivers, highlighting the role of not only climate change (hazard), but particularly population growth (increase in exposure) and adaptation (decrease in vulnerability). Further integration with available operational services, primarily the Copernicus Climate Change Service, would enable timely input data processing for the hydrological and hydrodynamic modelling of riverine and coastal flooding. The approach will be extended to multihazard events, which will be showcased through the use case of extra-tropical cyclone Xynthia, which resulted in major impacts from both coastal flooding and extreme wind speeds in France in 2010.

How to cite: Paprotny, D., Tilloy, A., Terefenko, P., Mengel, M., and Couasnon, A.: Impact attribution of European floods: towards an operational system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4431, https://doi.org/10.5194/egusphere-egu25-4431, 2025.