Global warming is reshaping the water cycle, driving changes in the intensity, seasonality and timing of precipitation extremes. These shifts have far-reaching consequences for flooding, soil erosion, landslides, and debris flow, requiring a comprehensive analysis of both historical trends and future projections. The research presented here integrates historical observations, advanced statistical methods and climate model simulations to assess global and regional changes in precipitation extremes. The work reveals how the timing and seasonality of historical precipitation extremes have already shifted in many regions. Future projections suggest a robust increase in the magnitude of precipitation extremes, particularly under high emission scenarios, and that the rarest extremes are expected a further intensification compared to more common ones. The results show large spatial variability, emphasizing the importance for regional climate adaptation strategies.
How to cite: Gründemann, G., van der Ent, R., van de Giesen, N., Brunner, L., Zorzetto, E., and Clark, M.: Changing precipitation extremes on the global domain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5026, https://doi.org/10.5194/egusphere-egu25-5026, 2025.
Understanding trends in rainfall extremes is essential for managing hydrological risks and designing climate-resilient infrastructure. Among the European countries, Italy presents a complex case study for such analyses due to its diverse topography, ranging from sea-level plains to alpine peaks, and its fragmented hydrological datasets.
This study investigates trends in short-duration rainfall extremes (1 to 24 hours) across Italy using annual maximum rainfall depths measured by rain gauges, coming from the Improved Italian - Rainfall Extreme Dataset (I2-RED), a collection of more than 5000 time series spanning the period 1916–2022.
Two complementary methodologies were employed. The Mann-Kendall test was initially applied together with the evaluation of the Sen’s slope to assess trend significance and magnitude. This approach, however, suffered the presence of highly-fragmented time series, covering different periods. Then, to address data fragmentation, a distributed quantile regression approach was used, pooling data within defined radii around grid cells. This latter approach ensured consistency across regions with different spatial and temporal densities while maintaining sensitivity to local variations and allowed for a robust analysis of trends in the median (0.5 quantile) and higher quantiles (0.95 and 0.99), enabling the identification of spatially coherent clusters of positive and negative trends.
The results reveal substantial variability across regions, with higher quantiles showing more pronounced changes than the median, indicating faster changes in extreme events compared to more ordinary rainfall. The median values of the 1h annual maxima show an increase all over the country. For the 24h duration, opposite tendencies can emerge even at close distances. The findings emphasize the spatial heterogeneity of rainfall trends in Italy and their implications for hydrological design.
How to cite: Claps, P., Mazzoglio, P., Ganora, D., and Viglione, A.: Analyzing trends in ordinary and extraordinary rainfall extremes by using fragmented records, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13251, https://doi.org/10.5194/egusphere-egu25-13251, 2025.
Floods are one of the most common natural hazards in the UK. Significant changes in flood behaviour in the UK have been observed in previous research, including notable shifts in flood frequency, magnitude, and timing. Flood hazards are increasing in some regions, with high-magnitude floods occurring over shorter durations (i.e., becoming ‘flashier’). These flashier floods develop rapidly and tend to have high peak flows, leaving less time for flood warnings and emergency response, which can potentially lead to more severe impacts. However, studies on flood flashiness are limited. Our study aims to investigate changes in flood flashiness across the UK using an event-based approach. A total of 158,682 individual flood events were identified across 354 selected UK catchments between 1980 and 2019. Two flashiness metrics, the flashiness index (FI) and rate of change (RoC), were employed to quantify the rapidity of river flow changes during flood events, with FI measuring flow variability and RoC assessing the rate of flow increase. Statistical methods (e.g., Mann-Kendall test and Theil-Sen estimator) were applied to detect trends in flashiness across all the study catchments. Spatial patterns of flashiness changes were examined on both long-term and seasonal scales. In addition, correlation analysis was performed to explore potential relationships between flashiness patterns and catchment attributes (e.g., catchment area, slope, longest drainage path, and the degree of flood attenuation due to lakes and reservoirs). Preliminary results indicate that increases in flood flashiness were regionally focused in some cases, but also spatially heterogeneous in many others. Flood events mostly occur in winter. accompanied by significant trends in flashiness changes that diverge in direction regionally, with northeast Scotland experiencing significant increases, whereas most other regions showing significant decreases. The two metrics, which capture different aspects of flashiness, correlate with various catchment attributes (e.g., FI decreases with increasing catchment area). Moreover, rainfall patterns, pre-event soil moisture conditions, and urbanization are likely key factors influencing the observed geographical and temporal variations. Further exploration is required to understand how these factors influence the observed patterns and how their interactions contribute to changes in flashiness. These insights are expected to inform the development of more effective flood management strategies.
How to cite: Yang, C., Han, S., Larsen, J., Slater, L., Hannaford, J., and Pope, E.: How has flood flashiness evolved across the UK?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17678, https://doi.org/10.5194/egusphere-egu25-17678, 2025.
Climate change and anthropogenic influences alter the primary flood drivers, such as sea surge, rainfall, and river flow, leading to shifts in flood risk patterns. The traditional assumption of stationarity in flood risk assessments is increasingly inadequate, as it fails to account for the dynamic interactions between these drivers. This study presents a framework to evaluate the potential for compound floods under non-stationary conditions, which considers the changing dependencies and risks between sea surge, river flow, and rainfall. The framework employs dynamic copulas to capture time-varying relationships and assess the compounded risk of multiple flood drivers.
The proposed model is applied to Indian estuaries, focusing on east- and west-flowing rivers contributing to the Arabian Sea and the Bay of Bengal. By examining flood events in these regions, the study demonstrates how the potential for compound flooding is amplified under non-stationary conditions compared to traditional stationary assumptions. The results reveal that the compound flood potential increases by 11% to 18% across Indian estuaries, indicating heightened vulnerability to extreme flooding events. This finding underscores the need for updated risk assessments that incorporate non-stationarity, particularly for coastal regions, where the interplay of climatic and hydrological variables is increasingly complex.
The study highlights the importance of adopting non-stationary models for flood risk evaluation in light of changing environmental conditions. By integrating dynamic copula-based approaches, this research offers a more accurate and practical framework for understanding and mitigating compound flood risks in the context of climate change.
How to cite: Bhere, S. and Reddy, M. J.: Evaluating Compound Flood potential in Indian Estuaries under Non-Stationary Climate Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19670, https://doi.org/10.5194/egusphere-egu25-19670, 2025.
Extreme rainfalls and floods have caused severe socio-economic and environmental losses in most parts of the world and are predicted to exacerbate due to the changing climate. Highly saturated soil, extreme rainfall, and heavy snowmelt are the most common flood triggers. However, the relative contributions of extreme rainfall, excess soil moisture, and snowmelt and how they are vary with time and change from catchment to catchment are not fully understood. This information is critical for a better understanding of flood generation mechanisms and can improve flood risk management plans and strategies. We examined precipitation, streamflow, and soil moisture at daily time scale from more than 671 hydrological stations across the country. Our main objectives were creating flood driving mechanisms according to hydrometeorological characteristics, identifying the contribution of independent variables (excess soil moisture, snowmelt, extreme rainfall) and understanding the spatial and temporal variability of mutual information.
The relative importance of each variable and its respective flood-generating processes were identified by combining the multilinear regression and ANOVA approaches. we confirm that most of peak flows are strongly associated with both the extreme rainfall (67%) and excess soil moisture (26%) conditions. There is a clear difference in flood magnitude and their respective generating mechanisms between regions, and regions with an expected decrease in soil moisture into the future were highly statistically correlated with a decrease in annual average peak flood magnitude. The role of extreme rainfall is the most dominant factor across the UK; however, seasonal total rainfall is not a strong influencing factor of peak floods in the southern UK. Extreme rainfall and peak floods are positively corrected with catchment drainage area. This linkage between drainage area and the most common flood generation mechanisms is crucial to quantifying the magnitude and level of flood risk in ungauged catchments.
How to cite: Meresa, H., Griffin, A., Kay, A., and Hannaford, J.: Identifying the drivers of flood generation mechanisms and their seasonal variabilities. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11391, https://doi.org/10.5194/egusphere-egu25-11391, 2025.
The impact of climate change on flooding is quite contrasted in different regions, and often the trends observed can be explained by differentiated changes in flood-generating processes. This study is based on an unprecedented exercise to model the impacts of climate change on hydrology in France, using a semi-distributed model (GRSD) applied to 3727 basins with 22 climate simulations for two greenhouse gas emission scenarios, the RCP4.5 and RCP8.5. Annual maximum flows were extracted for the period 1975-2100, and trend analysis was carried out on both flood magnitude and flood generation processes. Trends in flood magnitude are contrasted, with increasing trends only in the northern half of France, but multi-model convergence rarely exceeds 60%. The increase is greatest for the rarest floods and under the RCP8.5 scenario. For the southern regions, there is an overall decreasing trend in flood magnitudes. Antecedent soil moisture trends follow the same spatial pattern, with an increase mainly in the north-east of France, and decreasing trends in southern basins. The fraction of direct runoff contribution during floods rises sharply in the northwest and the Alps mountains, while the contribution of snowmelt is decreasing in all mountainous regions. Regarding changes in flood-generating processes, the proportion of floods linked to soil saturation excess is increasing mostly in north-eastern France, while decreasing in the south. Conversely, the ratio of floods induced by short and intense rainfall events is increasing in southern and north-western France, most notably under the RCP8.5. The number of rain-on-snow and snowmelt-driven episodes is decreasing across the whole country. This type of approach makes it possible to disentangle the relative influence of different flood-generating processes on trends in flood risk and consequently attribute these changes.
How to cite: Tramblay, Y., Thirel, G., Strohmenger, L., Heraut, L., and Sauquet, E.: Changes in flood-generating processes in France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8590, https://doi.org/10.5194/egusphere-egu25-8590, 2025.
Flood retention basins are a crucial element in flood protection strategies. Local studies have clearly demonstrated their effectiveness in significantly reducing flood discharges, thereby minimizing potential downstream damage. However, the impact of these retention basins on reducing flood discharges at the large river basin scale remains unclear.This study investigates the influence of reservoirs and dams on flood discharge reduction in Germany. Using the spatially distributed hydrological model SALTO, daily discharges from over 1500 gauged catchments between 1951 and 2020 were simulated. The model incorporates the effects of 530 reservoirs and flood retention basins on daily runoff volumes. Calibration at 700 gauging stations allows for regional parameterization of the model based on the PASS method. The study examines various scenarios, including the absence of reservoirs for flood protection and changes to the storage capacity and function of individual reservoirs. It not only assesses the reduction of runoff peaks but also analyzes changes in the duration of individual flood events and their spatial extent, considering the complex network of 530 reservoirs. In essence, this research contributes to the ongoing discourse on the effectiveness of flood retention basins and provides insights into the nuanced roles of reservoirs and dams in shaping the hydrological landscape. The findings offer valuable guidance for optimizing flood protection strategies, considering storage capacities, operational functions, and the broader spatial and temporal aspects of flood events.
How to cite: Merz, R. and Merz, B.: Reshaping the Flood? - Analyzing the Large Scale Impact of Reservoirs and Dams on Flood Reduction in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12262, https://doi.org/10.5194/egusphere-egu25-12262, 2025.
China is severely impacted by extreme precipitation and flooding, with annual average direct economic losses from flooding exceeding $30 billion over the past few decades. To safeguard the population and assets, China has constructed numerous hydraulic infrastructure projects, with the total reservoir capacity doubling nationwide from 1991 to 2022. However, while many studies have explored the impact of extreme precipitation on flood losses, the specific effects of hydraulic infrastructure on these losses remain inadequately quantified. In this study, we employ statistical methods to analyze the influence of extreme precipitation and reservoir capacity on flood loss rates at the provincial level from 1991 to 2022. Our results show that both extreme precipitation and reservoir capacity significantly affect flood losses in most provinces (p < 0.05), with opposite directional effects. This study demonstrates that incorporating the effects of hydraulic infrastructure significantly improves the accuracy of flood loss assessments, underscoring the importance of including human activities in flood risk evaluations.
How to cite: Cui, S. and Zhao, J.: The effects of extreme precipitation and reservior construction on flood losses in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6865, https://doi.org/10.5194/egusphere-egu25-6865, 2025.
Flood impacts are expected to exacerbate due to climate change and socio-economic development, and more efforts in adaptation measures are required to meet future challenges. Although it is believed that frequent exposure to flooding contributes to adaptation, it is still unclear whether or not we are well-adapted to flood events generated by the most common processes. Here we analyze counterfactual impact footprints of flood events generated by different processes in 971 European catchments for the period 1960—2010. We show that flood experiences of the most common processes do not guarantee better adaptation for them due to the failure to transform frequent flood experiences into effective targeted measures and the limitations of learning from experiences. Hence multiple flood characteristics generated by diverse processes still shape fatality and economic losses in Europe. Long rainfall on dry and wet soils and snowmelt are more impactful flood generation processes in the Atlantic and Central-Alpine, Mediterranean, and Northern regions.
How to cite: Yu, J., Paprotny, D., Merz, R., and Tarasova, L.: Flood generation processes shape fatality and economic losses in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6966, https://doi.org/10.5194/egusphere-egu25-6966, 2025.
The idea that extreme river floods are intrinsically different from smaller, more frequent floods underlain the estimation of possible flood magnitudes for decades, and was further advocated in recent years. At the same time, newly developed approaches proved the possibility of predicting the occurrence of extreme floods based on the features of more ordinary runoff events, in a way refuting the initial claim.
Here we give an overview of these approaches that enable inferring extreme floods from everyday hydrologic dynamics, by focusing on recent results by the authors. The methods, which are rooted in the Physically-based (PhEV) and Metastatistical (MEV) Extreme Value distributions, account for the role of antecedent catchment conditions (considered stochastically) and runoff generation processes in shaping the flood hazard.
We show that the possible occurrence of extreme floods and the emergence of heavy-tailed flood distributions and flood divides (i.e., marked increments of the magnitude of rarer floods) are predicted by metrics of everyday discharge dynamics. We present how knowledge of runoff generation processes can be used in the MEV framework to predict extraordinarily large river floods. We finally show that combining the MEV and PhEV frameworks allows for obtaining reliable estimates of rare floods with no need of a careful preliminary choice of the distribution of ordinary events with a particular tail, currently a critical step of the MEV approach.
How to cite: Basso, S., Wang, H.-J., Mushtaq, S., Devò, P., Miniussi, A., Tarasova, L., Merz, R., Marani, M., and Marra, F.: Inferring extreme river floods from everyday hydrologic dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6310, https://doi.org/10.5194/egusphere-egu25-6310, 2025.
The heavy-tailed behavior of floods indicates that the occurrence probability of extreme floods is greater than that predicted by the commonly used distributions with exponential asymptotic behavior, But in practical engineering applications, the heavy tail of floods in a basin may be caused by overestimating the frequency of extreme floods in that basin. The difficulty in predicting the degree of heavy tail leads to relatively large errors in the general empirical flood frequency curves. On the other hand, regional envelope curves are widely used to characterize the flood potential of various regions worldwide. We have developed a new method that combines regional flood envelope curves to predict and correct the appropriate flood magnitudes in similar regions, including dividing regions based on hydrological information and estimating the appropriate range of predicted floods through quantile regression. Flows exceeding this range are considered extreme and overestimated, while those below this range are considered to neglect the heavy tail and underestimated. In this study, we used flood data from at least 2,000 stations across China. Although the heavy tail of floods affects the flood frequency curve, the new method that combines regional flood envelope curves can better estimate the empirical frequency curve of the basin.
How to cite: Ding, C. and Yang, H.: The method of predicting heavy tail of flood combined with regional envelope curve, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4008, https://doi.org/10.5194/egusphere-egu25-4008, 2025.
The Yangtze River Basin (YRB) has repeatedly witnessed devastating, widespread floods. While extensive research has been devoted to understanding the impact of large-scale synoptic conditions and river regulations on these events, the role of storm dynamics has received much less attention; this is also true for a comprehensive comparative analysis among these historic floods, crucial for flood protection. Here, we employ rainfall and flood observations, reanalysis datasets, and large-scale hydrodynamic simulations to revisit the historic Yangtze River floods, with a special focus over middle and lower reaches. We find that these disastrous Yangtze River floods are a product of persistent heavy rainfall during the warm season and anomalously wet antecedent condition. The 1954 flood, for instance, is characterized by anomalous timing of the rainfall peaks, early across the upper reaches and delayed at middle-lower reaches. This leads to synchronized and elevated flood peaks along the main streams. We discern a preferential direction of storm movement for the 1954 flood, aligning perfectly with the river flow along the main tributaries. We will quantify the contributions of storm motion and timing to flooding in the YRB through space-time decomposition and hydrodynamic simulations for several historic flood events.
How to cite: Yang, Y., Yang, L., Villarini, G., Shen, Y., and Zhao, F.: The historic Yangtze River floods: Role of multiscale storm movement, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10127, https://doi.org/10.5194/egusphere-egu25-10127, 2025.
Mountains are sensitive to the increase in air temperature because it causes a shift from snowfall to rainfall, resulting in a decrease in snow storage. This further affects the runoff regime, including runoff extremes such as rain-on-snow (RoS) events and floods. In this study, we attributed these climate changes to simulated variations in RoS events using a sensitivity analysis of precipitation and air temperature and evaluated the subsequent effects on ROS-related runoff responses. We selected 93 catchments in Czechia and Switzerland, representing both high alpine and rain-snow transition areas. We used a conceptual catchment model to simulate snow storage and runoff for the reference historical period and for the ensemble of 24 climate perturbations reflecting the expected increase in air temperature and changes in precipitation.
Changes in RoS due to climate change were highly variable between regions, between elevations and within the cold season, with RoS occurring most frequently at elevations between 1000 and 2000 m a.s.l. RoS days are expected to become less frequent with future increases in air temperature, especially at lower elevations. The +4°C perturbation suggested a decrease in RoS days by about 75 % for the Czech catchments. In contrast, the Swiss catchments may respond less sensitively, with the number of RoS days even increasing, especially during the winter months and at higher elevations, which may be further enhanced by increased precipitation. The contribution of RoS events to total annual runoff is expected to decrease from 10% to 2-4% for the +4°C perturbation in the Czech catchments and from 18% to 5-9% in Switzerland. However, the contribution of RoS to runoff may increase in winter months, especially for climate perturbations leading to an increase in precipitation, demonstrating the joint importance of air temperature and precipitation for future hydrological behaviour in snow-dominated catchments. The results have important implications for climate change adaptation strategies, such as water management, flood and drought protection, or hydropower.
How to cite: Jenicek, M., Hotovy, O., Nedelcev, O., and Seibert, J.: Rain-on-snow runoff events in mountainous catchments under climate variability and change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4433, https://doi.org/10.5194/egusphere-egu25-4433, 2025.
Soil moisture, a key hydrologic variable that determines hydroclimatic extremes including catastrophic flooding, is generally considered less important in cities compared to natural, agricultural and rural areas due to the prevalence of impervious surfaces. However, recent empirical studies on stormwater runoff reduction by turfgrass lawns (which cover nearly half of the cumulative urban area in the USA), have demonstrated that soil moisture is a highly significant factor in urban flooding. Yet, urban soil moisture remains highly uncertain due to the high heterogeneity of urban land cover and irrigation practices. Although in-situ soil moisture data are very limited in urban areas, satellite-based soil moisture products provide spatiotemporally continuous datasets worldwide. However, existing products are subject to substantial errors in urban areas due to the complexity introduced by a combination of impervious surfaces, built structures, green spaces and the effects of landscape management activities, especially irrigation. Post-processing of satellite-based soil moisture promises to resolve this issue and help improve the accuracy of urban soil moisture products. Here, we present a machine learning (ML)-based framework for post-processing satellite-based soil moisture products in urban areas. Using this framework, we post-processed the European Space Agency’s Climate Change Initiative (CCI) daily soil moisture product in two cities in the USA with contrasting geographic locations, climates and vegetation covers: Los Angeles and Tallahassee. Los Angeles is located in the State of California on the West Coast and has a semi-arid climate and mild vegetation cover, while Tallahassee is located in the State of Florida on the East Coast and has a humid subtropical climate and very high vegetation cover. Land surface characteristics (imperviousness and Normalized Difference Vegetation Index—NDVI), precipitation and air temperature were used as inputs in the ML model. The model performance was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). Our findings indicate that ML-based post-processing substantially improved the accuracy of CCI soil moisture products in both cities. At five monitoring sites in Los Angeles, R2 increased from 0.00-0.29 to 0.81-0.86 and RMSE decreased from 0.06-0.15 m3/m3 to 0.02-0.07 m3/m3. At the three monitoring sites in Tallahassee, R2 increased from 0.01-0.07 to 0.88-0.92 and RMSE decreased from 0.06-0.12 m3/m3 to 0.01-0.02 m3/m3. Our analysis also revealed that five-day antecedent precipitation had the greatest importance for improving the satellite-based soil moisture data at the sites in Los Angeles and Tallahassee. The framework developed in this study can be used to improve the accuracy of other satellite-based soil moisture products and advance urban flood projections around the world.
How to cite: Ahmadisharaf, E., Shayeghi, S., Litvak, E., Rahimi, L., and Moradkhani, H.: A Machine Learning Framework for Post-processing Satellite Observations of Soil Moisture in Urban Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14818, https://doi.org/10.5194/egusphere-egu25-14818, 2025.
The classification of Finland's watersheds based on morphological and environmental characteristics into hydrological sensitivity levels is crucial for understanding and managing the country's diverse catchment areas. In climate research, hydrological sensitivity often refers to the relationship between precipitation and temperature increases. However, in this study, hydrological sensitivity is defined as the degree to which Finland's watersheds respond to environmental factors and changes, such as variations in water flow and runoff. This sensitivity is influenced by several factors, including the morphometric characteristics of the watershed, soil type, land cover, and lake coverage, which determine how water flows, is absorbed, and is retained within the watershed, impacting runoff patterns, flood potential, and sediment transport.
This study adopts a national-level multiscale approach to categorise catchments using a combination of morphological and environmental variables. By integrating these factors, we seek to describe the variation within Finnish watersheds and classify them into various sensitivity levels. Data were sourced from the Finnish Environment Institute (SYKE) and the Geological Survey of Finland (GTK). Watershed boundaries, morphometric data, soil type, land cover, and lake coverage were analysed. Morphometric analysis included calculations of basin geometry, stream network characteristics, stream texture, and relief parameters.
Principal Component Analysis (PCA) was utilised to reduce the dimensionality of the dataset and identify the most influencing variables contributing to watershed sensitivity. The PCA approach determined the strongly correlated components, and the weight of each variable was determined using the weighted sum approach method. Compound values were then calculated based on the weighted values and preliminary ranking to indicate the hydrological sensitivity levels, which were divided into five classes—ranging from very low to very high.
The results were visualised through maps and charts, highlighting hotspots of hydrological sensitivity. This research provides valuable assessments into the hydrological characteristics and behaviour of Finland's watersheds, supporting targeted interventions for effective environmental management and informed policy-making.
How to cite: Akinyemi, T., Kankare, V., Nylén, T., and Alho, P.: Hydrological Sensitivity Mapping: Insights into Finland's Watershed Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19860, https://doi.org/10.5194/egusphere-egu25-19860, 2025.
India is infamous for the highest proportion of its population that is exposed to direct and indirect flood impacts. Despite disaster looming situations, several flood-prone basins in the country still lack adequate ground-based coverage of gauge stations; thus, hindering our comprehension of flood impacts via numerical flood modelling. Reanalysis datasets, an advancement from Earth Observation Datasets (EOD), emerge as a valuable substitute for sparse ground-based observations as they furnish relevant atmospheric and hydrological variables at high spatio-temporal resolutions. This study evaluates the efficacy of runoff and rainfall estimates from high-resolution ERA-5, JRA-55, CFSR, and MERRA Hydrological Reanalysis Data (HRD) across India for capturing flood inundation and hazards. The runoffs and rainfalls in each Reanalysis dataset are compared with the ground-based observations using various metrics such as correlation coefficient (CC) and Kling-Gupta efficiency (KGE). In the next step, they are considered as primary boundary conditions along with other ancillary datasets to LISFLOOD-FP, a global hydrodynamic flood model, to derive high-resolution flood maps for specific flood events. The simulated flood inundation maps are calibrated and validated against past flood incidences derived from satellite altimetry using performance indices including Hit-Rate (HR), False Alarm Ratio (FAR), Error Bias (EB), and Critical Success Index (CSI). Subsequently, the best-performing HRD for a specific basin is utilized to derive distributed design input values through extreme value analysis for various scenarios (e.g., 1 in 50-yr, 100-yr, and 200-yr). These distributed discharges are fed to LISFLOOD-FP to generate high-resolution flood inundation and hazard maps. The study, for the first time, determines the efficacy of Reanalysis products in flood mapping over data-limiting large watersheds, thus providing a solid foundation for stepping up for quantifying flood risks, even under changing climate conditions.
How to cite: Singh, H. and Mohanty, M. P.: Suitability of Reanalysis products in capturing Flood Inundation and Hazards over India: Deriving insights through Statistical tests and Numerical Flood Modeling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-755, https://doi.org/10.5194/egusphere-egu25-755, 2025.
Understanding how climate change affects the frequency and magnitude of floods is essential for adaptation strategies. Usually, the impact of climate change on extreme weather and floods is assessed using the projections of the Regional Climate Model (RCM) in a hydrological model. Nevertheless, RCM projections are usually too coarse to describe convective storms, and they can underestimate the intensity of short-duration extreme precipitation that is usually responsible for flash flood events in small river basins. For this reason, Convection Permitting Models have been proven to perform better than RCM in describing sub-daily extreme precipitation.
The objective of the work is to assess the impact of climate change on the flood frequency of the Arno River basin using CPM projections to also better describe future flood hazards in its small tributaries. The hydrological model used in the analysis is the Soil and Water Assessment Tool Plus (SWAT+). The projections used as input of the model are VHR-PRO_IT, Very High-Resolution Projections over Italy (Raffa et al., 2023). The projections have a 2.2 km spatial resolution and 1h temporal resolution, and they cover 90 years from 1981 to 2070 in the emission scenarios RCP 4.5 and RCP 8.5. First, the model has been calibrated with 15 years of observed data. Then, the projections have been bias-corrected and used as input in the continuous hydrological model. Therefore, SWAT+ performs a continuous hydrological simulation for 90 years at the hourly timestep.
The bias correction of the precipitation projections has been done with a parametric approach (Mamalakis et al., 2017) to adjust the frequency distribution of precipitation events. The correction of temperature projections has been done with an easier approach based on the linear scaling of monthly average temperatures. The bias correction used ground observations of rain gauges and thermometers of the Hydrological Regional Service of the Tuscany Region.
The results are expressed with a delta-change approach to extract possible trends in the simulated discharges. The delta change expresses the ratio between the peak discharge associated with a given return period T in the future and the peak discharge for the same frequency in the historical period. The Generalized Extreme Value Distribution (GEV) is used to fit the cumulative distribution of the annual maximum series for three-time windows of 30 years: 1981-2010 (Historical), 2011-2040 (Near Future), and 2041-2070 (Far Future). The results show an increase in the flood hazard in the city of Florence in the RCP8.5, especially in the far future.
ACKNOWLEDGEMENT: The research is carried out within the RETURN – multi-Risk sciEnce for resilienT comUnities undeR a changiNg climate Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).
How to cite: Caporali, E., El Jeitany, J., Gordi, G., Deidda, R., Borga, M., and Lompi, M.: Future flood frequency curve of the Arno River (Central Italy) by using bias-corrected convection-permitting model projections in a semi-distributed hydrological model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17805, https://doi.org/10.5194/egusphere-egu25-17805, 2025.
Hydrology science provides different methods to estimate infiltration: physically based, semi-empiric, and empiric models. Several researchers have compared the adequacy of these models to different conditions, such as soil types and land uses. Most of them were applied to agriculture and natural/forest lands. One of the main conclusions provided by such studies is that land cover is the most relevant factor, even more than soil texture. As a result, infiltration estimates in urban areas are subject to significant uncertainty. Some of the most employed models in infiltration evaluation are Green-Ampt (GA), Horton, Kostiakov and Philip. GA and Horton methods are particularly prevalent in applications involving urban areas. In addition, some studies showed that combining infiltration methods with flow redistribution models could improve the infiltration analysis performance.
In addition, in the last few years, 2D hydraulic models have incorporated improved functionalities to simulate urban floods generated by short and high-intensity storm events in small catchments. Such models usually include both GA and curve number (CN) infiltration equations for characterizing infiltration processes, obtaining runoff amounts and simulating flooding water depths, velocities and extents.
In this study, we assessed the hydrological response of urban basins in flood events in terms of the selected infiltration equation. We employed two 2D hydraulic models, HEC-RAS 2D and IBER, to analyze the hydrological response of an urban catchment in the city of Pamplona (Spain). We compared the results of two infiltration equations, GA and CN, considering different rain patterns. Firstly, we used twenty-one synthetic hyetographs ranged from 2-yr to 200-yr return period and three hyetograph peak positions (centered, left-skewed and right-skewed). Secondly, we also considered the precipitation fields with a time step of 10 minutes for two real storms. The results show that the GA model provides larger flooding extents than the CN model. In addition, the IBER model simulates larger flooding extents than the HEC-RAS 2D model. Differences appear to be more significant for smaller total rainfall depth storms.
How to cite: Fernández-Fidalgo, J., Bianucci, P., Soriano, E., and Mediero, L.: Evaluating infiltration models for urban flood assessment: a case study of Pamplona, Spain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7259, https://doi.org/10.5194/egusphere-egu25-7259, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot A
EGU25-18401 | ECS | Posters virtual | VPS11
Modeling Flash Flood Events in The Arid And Semi Arid Regions of The Luni River Basin IndiaFri, 02 May, 14:00–15:45 (CEST) | vPA.9
The Luni River Basin situated in the arid and semi-arid regions of Rajasthan, faces growing challenges related to flooding, despite receiving low annual rainfall, with some areas recording less than 250 mm. The Luni being an ephemeral river, is primarily influenced by monsoonal precipitation which drives the majority of surface runoff within the basin. However, the increasing frequency and intensity of extreme rainfall events have significantly altered its hydrological dynamics. These sudden and intense downpours increasingly trigger flash floods, which disrupt the already fragile water dynamics of the region. Flood events in the Luni Basin are particularly severe due to the interplay of geomorphological and anthropogenic factors. The basin predominantly has sandy soil, coupled with high salinity levels result in limited infiltration capacity. This, combined with enhanced surface runoff exacerbates the frequency and impact of floods. Moreover, extensive groundwater extraction, rapid land-use changes, urbanization, and the expansion of irrigation systems reliant on canal-fed networks have heightened the basin’s susceptibility to flooding. These floods not only damage critical infrastructure and agricultural lands but also complicate water storage and long-term resource management strategies. This study focuses on modeling the flash flood events in the Luni River Basin over the period from 1979 to 2024 to better understand their impacts on the arid and semi-arid regions of Rajasthan. Advanced hydrodynamic models, such as HEC-RAS and ANUGA, have been utilized to simulate these flood events, providing a detailed representation of flood behavior and extent. The accuracy of these models has been enhanced through validation against satellite-derived data for recent events. This ensures reliable flood extent mapping, offering valuable insights into the basin's hydrological responses and supporting the development of effective flood mitigation and management strategies.
How to cite: Dahiwale, A. V., Nema, S., Jatav, M. S., Barman, D., Choudhary, S. S., Rao, M. S., and Sharma, A.: Modeling Flash Flood Events in The Arid And Semi Arid Regions of The Luni River Basin India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18401, https://doi.org/10.5194/egusphere-egu25-18401, 2025.
