Orals: Mon, 28 Apr | Room 2.17
Extreme precipitation events are critical phenomena that pose significant risks to societies. Understanding their spatial patterns and drivers is vital for both scientific and practical purposes, such as risk management strategies. This study investigates the spatial correlation of extreme precipitation events across Europe, their modulation by the North Atlantic Oscillation (NAO), and the detection of potential changes over recent years. Additionally, it evaluates whether these patterns and trends are accurately replicated by tools commonly employed in the insurance industry.
Correlations between extreme precipitation events across European regions are investigated. In addition, the NAO, which is a dominant mode of atmospheric variability in the North Atlantic, is widely considered to be a significant modulator of these spatial patterns. Positive phases of the NAO are associated with intensified extreme precipitation in northern and western Europe, while negative phases shift these patterns towards southern Europe. By coupling precipitation data with NAO indices, we demonstrate how changes in NAO phases alter the spatial coherence and intensity of extreme events. Furthermore, a critical aspect of this study is comparing these patterns and trends with the tools and methods used in the insurance industry.
This study contributes to a better understanding of extreme precipitation dynamics in Europe, offering insights for practical applications in risk management. By highlighting gaps in current approaches, it underscores the need for integrating advanced climate diagnostics into risk assessment frameworks.
How to cite: Tsaknias, D.: Spatial patterns of extreme precipitation in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15034, https://doi.org/10.5194/egusphere-egu25-15034, 2025.
The assessment of extreme precipitation statistics is essential for managing flood hazards and developing effective climate change adaptation strategies. These design values are typically estimated through the frequency analysis of precipitation data, with limited understanding of their generative atmospheric phenomena. We aim to go beyond the statistical extrapolation of observed extremes with extreme value distributions towards enhancing their physical comprehension: this may be beneficial for improving our estimates of extreme precipitation probability and our predictions of future changes. Our analysis is based on a network of ∼300 rain gauges and temperature stations in a complex-orography region of the Alps. We estimate the magnitude of extreme precipitation from sub-hourly to daily durations for return periods up to 100 years (1% annual exceedance probability). We employ a non-asymptotic extreme value approach based on the concept of storms (independent meteorological objects) and ordinary events (duration maxima within each storm). We focus on the ordinary events exceeding high percentiles (e.g., 85th, 90th, 95th) at some duration, and we extract several characteristics of the corresponding storms, such as the event peak and average intensity, total lifetime, seasonality, temporal profile, peakedness, temperature, etc. We then assess their relationships with the parameters of our non-asymptotic extreme value model.
Our preliminary results show that variations in the model parameters depend on topography and event duration. Heavier tails in the extreme precipitation distribution emerge at sub-hourly durations in mountainous regions and for parts of the lowlands, but at longer durations in the pre-Alps. The scale parameter is generally higher in the lowlands and the pre-Alps. As a result, extreme precipitation intensity for short duration is generally higher in the lowlands than in the mountains (“reverse orographic effect”), with higher intensities in the pre-Alps at longer durations. Storm characteristics also vary with topography, precipitation duration, and event extremeness. In summer, front-loaded storms are prevalent at short durations, where heavier tails are observed. In the pre-Alps, storms are characterized by the highest extremes at long durations, have a more symmetric temporal profile, are most common in autumn, and have a longer total lifetime compared to the rest of the region.
Further investigation is needed to clarify the relationship between storm characteristics and statistical properties. This work enhances understanding of the key processes shaping precipitation extremes and provides insights for improving predictive models, ultimately aiding in risk assessment and climate resilience planning.
This study is carried out within the RETURN 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: Dallan, E., Marra, F., Papacharalampous, G., Fowler, H. J., and Borga, M.: Can the climatology of heavy storm characteristics explain extreme precipitation statistics?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5069, https://doi.org/10.5194/egusphere-egu25-5069, 2025.
Extreme precipitation events have become increasingly frequent and intense in recent decades, resulting in severe flooding and substantial socio-economic losses. These events are typically associated with intense weather systems that vary across numerous meteorological factors and exhibit significant temporal and spatial variability. A comprehensive understanding of the underlying processes and the identification of key meteorological factors driving extreme precipitation are critical for enhancing the accuracy of extreme rainstorm predictions and flood warnings.
This study utilized cumulative distribution function (CDF) analysis based on ERA5 hourly reanalysis data and employed the eXtreme Gradient Boosting (XGBoost) algorithm to identify the key meteorological factors contributing to 24-hour extreme precipitation across three distinct climatic zones in China. Additionally, forecasting models were developed to predict these events. The results highlighted the efficacy of this methodology and demonstrated its ability to achieve the following key advancements:
- Mapping data into the CDF space effectively addressed the challenges posed by the spatial heterogeneity in the value ranges of meteorological factors in regional system analyses, thereby significantly enhancing the spatial scalability of the predictive model.
- The integration of SHAP (SHapley Additive exPlanations) value interpretation with XGBoost successfully identified the critical meteorological factors influencing extreme precipitation events. This facilitated the construction of classification and regression models to predict both the occurrence and the return periods of these events.
- The application of SHAP values enhanced the interpretability of the "black-box" XGBoost model by incorporating physical insights and elucidating the interactions between different factors, thus providing valuable information for the construction and refinement of the final model.
In summary, this study presents a novel and interpretable machine learning framework for analyzing and predicting extreme precipitation events based on the CDF analysis. By effectively addressing spatial heterogeneity and enhancing model interpretability, the proposed methodology offers significant advancements in the prediction of extreme rainfall and associated flood risks, contributing to improved disaster preparedness and mitigation efforts.
How to cite: Wu, X., Jiang, Z., and Sharma, A.: Predicting extreme precipitation events using machine learning techniques based on cumulative distribution function (CDF) analysis of meteorological factors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13959, https://doi.org/10.5194/egusphere-egu25-13959, 2025.
Flooding represents a growing concern for the (re)insurance industry, with precipitation extremes as a key driver of flood risk. Some of the most destructive flood events in 2024 were driven by extreme rainfall occurrences, although with important differences in spatial and temporal scales (e.g. Dubai floods, Ex-Hurricane Debby floods in Canada, Central Europe Floods, Hurricane Helene flooding in Georgia and North Carolina, Valencia floods).
Ongoing climate trends introduce additional uncertainty in the estimates of intensity, frequency, and distribution of rainfall extremes, complicating their quantification and risk assessment. Understanding and modelling these extremes is critical for improving flood risk management and financial preparedness.
This study investigates rainfall extremes in the United States across various temporal scales, focusing on their role in different types of flood risks. We compare multiple statistical models to estimate extreme precipitation values, including approaches that incorporate climate trends. By analysing spatial and temporal patterns of extremes, we evaluate how well these models capture underlying processes and improve predictive accuracy.
Our findings suggest that integrating additional information about climate trends and hydrometeorological processes enhances the accuracy of extreme rainfall estimates, moving in the right direction, although given the rare nature of these extremes looking at historical data alone leaves space for future unexpected outcomes. These results provide valuable insights for improving catastrophe models and stress-testing (re)insurance portfolios.
How to cite: Nicotina, L., Jewson, S., Petrie, R., Cox, T., and Ball, P.: Rainfall Extremes in a Changing Climate: Implications for Flood Risk and (Re)Insurance, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18938, https://doi.org/10.5194/egusphere-egu25-18938, 2025.
Extreme sub-daily precipitation is difficult to anticipate and may cause flash floods, urban floods and debris flows, resulting in casualties and damage to infrastructure, homes, and livelihoods. With increasing temperatures, more moisture can be stored in the atmosphere, which means that there is potential for larger extreme events. Indeed, short-duration precipitation extremes are already increasing in magnitude, and return levels (i.e., magnitudes associated with low exceedance probabilities) are changing. Quantifying extreme short-duration rainfall return levels for the coming years is critical for decision making and for defining insurance premiums. However, the methods we typically use to derive rainfall return levels do not include the physics driving the processes, so they are not suitable for predicting future extremes. The TENAX model was recently proposed to address this issue. It uses knowledge of temperature-precipitation scaling rates and statistics to predict future return levels of short-duration extreme precipitation based on the future temperature shifts. It has been successfully applied to mid-latitude regions, but we do not currently know how it should be parameterized for other climates with different temperature conditions and different processes behind heavy precipitation, such as the tropics. We apply TENAX globally using a global hourly rainfall dataset (GSDR) and ERA5-land reanalysis temperature data. We assess whether the statistical description of precipitation and temperature hold in different climates. Using the longest recording stations, we perform a hind-cast to check the ability of this approach to predict extreme hourly precipitation return levels for the coming decade.
How to cite: Thomas, E., Borga, M., Vohnicky, P., and Marra, F.: Changes in hourly rainfall return levels due to temperature shifts: global assessment of the TENAX model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-711, https://doi.org/10.5194/egusphere-egu25-711, 2025.
Climate change is increasing the frequency and intensity of extreme weather events, posing substantial risks to densely populated countries in the Global South, particularly India. Heatwaves, droughts, and floods threaten water resources, agriculture, ecosystems, and human livelihoods especially heightening the vulnerability of urban areas. To mitigate these impacts, it is essential to assess climate variability trends, identify regional disparities, and evaluate associated risks. Thus, this study analyzes climate extremes across 22 river basins in India from 1951 to 2023, using 20 extreme climate indices for precipitation and temperature. The spatial and temporal trends of precipitation and temperature are evaluated using the Modified Mann-Kendall (MMK) test, Sen’s slope estimator, and Innovative Trend Analysis (ITA). The vulnerability of 592 Indian cities to extreme climate events is ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The findings reveal significant regional disparities. Half of the river basins show declining monsoon and annual precipitation, with snow-fed basins like the Indus and Ganga experiencing reduced post-monsoon rainfall. Rain-fed basins of Godavari and Narmada are facing longer dry spells, while the Indus basin is experiencing more intense, short-duration rainfall. Maximum temperatures are rising across most regions, although colder winters persist in the eastern basin of Brahmani and Baitarani. An interesting observation is the lack of significant trends in precipitation and temperature in smaller river basins. Further, the urban risk analysis highlights Ganga (largest river basin in India) as most vulnerable, inhibiting 22 out of 25 most-affected cities. In contrast, Bongaigaon town, situated in the Brahmaputra River basin, was found to be the least affected. The river basin of the East flowing river between Pennar and Kanyakumari showed the lowest risk of increasing climate extremes, with six of the top 25 least-affected cities situated in this region. This study combines diverse climatic datasets and robust methodologies to shed light on regional vulnerabilities and urban risks, offering a foundation for designing targeted adaptation strategies tailored to the needs of different regions in India.
How to cite: Roy, S. and Goyal, M.: Climate Variability and Extremes in Indian River Basins: Trends, Regional Disparities, and Urban Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1114, https://doi.org/10.5194/egusphere-egu25-1114, 2025.
Hydrological extremes pose significant challenges to flood risk assessment and mitigation, particularly under non-stationary climatic and hydrological conditions. Hydraulic structures, such as large reservoirs, modify flood distributions by attenuating peak flows, with their effectiveness varying over return periods. This variability introduces non-stationarity in flood frequencies and has a significant impact on the tail of the distribution. As a result, data-driven approaches to flood frequency estimation can lead to under- or overestimation of flood quantiles, especially when limited observations are available. To address these challenges, we propose an analytical framework capable of defining the full probability distribution of floods at a control section. This method explicitly incorporates key physical processes, including the influence of reservoir volume, non-linear spillway behavior and threshold discharge on inflow hydrographs. The accuracy of the estimations is demonstrated by comparisons with numerical simulations of reservoir routing using the continuity equation in a real case study. Our results highlight the critical role of integrating physical processes into flood modelling to capture tail behavior, and show how statistical approaches applied to small samples of flood peak observations can instead lead to significant biases. The proposed analytical solution provides a robust and parsimonious tool for estimating the impact of reservoirs on floods, with applications in both risk assessment and infrastructure planning.
How to cite: Cipollini, S., Volpi, E., Vorogushyn, S., and Fiori, A.: Estimation of flood peak distributions considering reservoir effects on tail behavior, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20629, https://doi.org/10.5194/egusphere-egu25-20629, 2025.
Global climate change poses critical challenges to food security and market stability as extreme weather events become increasingly frequent and severe. The combined effects of compound extreme events and global trade dynamics on food security, however, remain insufficiently explored. Here, we employed a copula-based statistical approach, integrating international trade data to estimate maize yield failures under compound drought and heat events (CDHEs) and to assess how global trade dependence and supply diversity impact food security under such stressors. Our findings reveal a 70.1% probability of global maize yield failure as CDHE intensity increases, with key breadbasket regions, including Northeast China, Europe, North America, Latin America, and South Africa, particularly vulnerable. Both drought and heat events contribute similarly to global maize yield risk; however, regional desynchronies, such as distinct effects in China and Brazil, highlight differing vulnerabilities. Furthermore, countries heavily dependent on imports from regions with high yield failure risk, such as Vietnam and Colombia, face an increased probability of maize yield failure exceeding 40%. Conversely, supply diversity offers a modest buffering effect, mitigating some adverse impacts of CDHEs, albeit with notable uncertainties. Our findings underscore the compounded vulnerability of maize yields to CDHEs, intensified by trade dependencies, while highlighting the potential for supply diversification to enhance resilience. Urgent adaptations, transformative strategies, and policy interventions are critical to mitigate cascading risks within the global food system, bolster resilience to climate change, and ensure food security.
How to cite: Liu, S., Shi, T., Zhang, W., Li, T., Wang, Z., and Ma, X.: Balancing risks: How global trade dependence exacerbates and supply diversity mitigates yield failures under compound drought and heat events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-732, https://doi.org/10.5194/egusphere-egu25-732, 2025.
Chile is one of the most vulnerable countries to the impacts of climate change. This suggests challenges to mitigate their impacts and adapt existing infrastructure. Despite a consensus that future climate change will lead to an increase in hydrometeorological extremes and the importance of including this factor in the hydrological design of hydraulic infrastructure, clear national guidelines on how to achieve and implement this in practice are still lacking. To address this gap, this study aims to align national hydrological design methodologies with international best practices by offering recommendations for addressing extreme precipitation and surface runoff generation.
Key considerations include the temporal and spatial scales of precipitation and temperature, and methodologies for flow estimation in gauged and ungauged basins. Dynamic modeling, statistical methods, and synthetic unit hydrograph approaches are explored, with applied examples highlighting the integration of climate change in estimating peak flows, extreme precipitation, and intensity-duration-frequency (IDF) curves.
Our results show that dynamic hydrological modeling yields projections with lower associated uncertainty by accurately capturing historical patterns. Dynamic models account for interactions such as antecedent soil moisture and snowline shifts during extreme events. For northern Chile, spatially distributed or semi-distributed models are recommended to capture the heterogeneity of extreme events. In contrast, statistical and synthetic hydrograph methods present limitations due to their reliance on historical precipitation-runoff relationships and lack of spatial heterogeneity.
Finally, the study underscores the need for flexible, transdisciplinary approaches to address future climate challenges, advocating for hydrological system modeling and a deeper understanding of processes driving extreme hydrometeorological responses.
How to cite: Vargas, X., Muñoz-Castro, E., Jorquera, J., Muñoz-Castro, O., Ricchetti, F., and Gómez, T.: Hydrological design of hydraulic infrastructure in a changing climate – Insights for practitioners in Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18901, https://doi.org/10.5194/egusphere-egu25-18901, 2025.
Posters on site: Mon, 28 Apr, 10:45–12:30 | Hall A
The increasing frequency of global extreme hydrological events has highlighted the critical need for reevaluating hydraulic structures’ safety design considerations, mainly through non-stationary hydrological time series analysis. This study, conducted in the Krishna River Basin of India, aims to develop a robust methodological framework for non-stationary analysis of extreme precipitation events, emphasizing the importance of accurate extreme event extraction. Accurate extraction of extreme events is crucial for non-stationary analysis, as it ensures that the events analyzed are truly extreme. This precision is vital for reliable predictions and effective safety design in the face of changing climatic conditions. The study is divided into two major parts. First, the block maxima and peaks over threshold (POT) methods for extracting extreme events were compared. In the block maxima approach, a block size of one year was considered, whereas, in the POT approach, three threshold selection methods were considered: percentile-based (90th, 95th to 99th percentiles), top 'n' values and graphical method. The graphical method was identified as the most effective, based on parameter stabilization, return value matching from two extreme value distributions, and Akaike information criterion (AIC), confirming its superiority in model fitting. With accurate extreme events extracted, the study proceeded to non-stationary analysis (NSA) using nine covariates, categorized into climate change, global warming, local temperature anomalies, and trends. A total of 23 stations were analyzed, identifying significant covariate combinations for each station through the lowest AIC values. NSA indicated that the selected covariates significantly influenced the non-stationary behaviour of extreme precipitation events. This study emphasizes the critical need for precise extreme event extraction in non-stationary analysis. The graphical method for threshold selection and identifying significant covariates offers a reliable approach to understanding and predicting extreme precipitation events.
How to cite: Dixit, S., Pandey, K. K., and Kumar, S.: Enhancing non-stationary analysis of extreme precipitation through a precise extreme event extraction approach., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-264, https://doi.org/10.5194/egusphere-egu25-264, 2025.
The assessment of crop water sustainability under future climate scenarios is crucial for coping with predicted water scarcity and for devising strategies to ensure global food security. In this context, the evaluation of crop water indicators generally relies on global scale projections of climatic and hydrologic variables which often provide divergent estimates of precipitation, potential evapotranspiration, and renewable freshwater rates, thus affecting the final evaluation of crop water needs and associated risks. In this work, by performing a multi-model analysis (considering four climate models and six water models from the Inter-Sectoral Impact Model Intercomparison Project ISIMIP2b), we (i) evaluate and map crop water needs and sustainability under current and future climate scenarios, (ii) quantify the uncertainty associated with the climate and impact model selection, particularly focusing on how such uncertainties propagate both in time (from 2000 to 2090) and in space (ranging from global scale to the smallest grid cell unit), and (iii) assess the major sources of uncertainty (global climate or water models). Our results reveal a trend of increasing water unsustainability under future scenarios, despite significant uncertainties across models. Hotspots of unsustainable water use are identified in the Mideastern United States, Central Europe, and parts of South America, where blue water demands are projected to increase by over 150% by the end of the century relative to the year 2000. At the global scale, variations in green and blue water footprints from the average across all models are between ±10% and ±30%, respectively. Such uncertainties are highly amplified as the spatial scale of analysis is increased. For example, country-scale variations in green and blue water footprints of ±25% and ±100% relative to the multi-model average are observed in the United States. Disagreement across global water models dominates global uncertainty for blue crop water use and sustainability calculations, while variability across climate models contributes more prominently to green water footprint uncertainty under severe climate change scenarios. This study emphasizes the critical role of uncertainty quantification in understanding the variability of crop water requirements, offering key insights for managing agricultural water resources under changing climates.
How to cite: Sun, Q., Bassani, F., and Bonetti, S.: Uncertainties in global climate and water models challenge future estimates of crop water use and sustainability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9612, https://doi.org/10.5194/egusphere-egu25-9612, 2025.
Climate change has significant impacts not only on natural and ecological systems, but also on socio-economic systems. In particular, the frequency, intensity, and duration of extreme events such as extreme floods, droughts, heat waves, and heavy rainfall are increasing in irregular patterns in many regions of the world. To address these challenges, it is essential to establish a scientific management system capable of predicting and preemptively responding to such extremes based on quantitative analyses of climate change. Therefore, this study aims to quantify and analyze spatiotemporal changes in extreme events for the South Korea using the extreme climate indices. We utilize long-term daily high-resolution and high-quality gridded meteorological data, which has been recently developed at a spatial resolution of 1/16° for the period 1973-2022. From this dataset, 8 temperature-related and 8 precipitation-related extreme climate indices are computed on a gridded basis. These 16 extreme indices were developed by Expert Team on Climate Change Detection and Indices (ETCCDI) and Korea Meteorological Administration (KMA). To evaluate changes in the intensity, frequency, and duration of extreme events, we compare the mean values of the extreme climate indices for two 25-year periods: 1973–1997 and 1998–2022. This analysis provides insights into the temporal and spatial variations and differences in extreme events. The findings of this study are expected to reveal the trends of extreme events in South Korea due to climate change. Furthermore, they will provide a scientific foundation for developing climate change adaptation and management strategies at both national and regional levels.
Acknowledgement
This work was supported by Korea Environment Industry & Technology Institute(KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Program(or Project), funded by Korea Ministry of Environment(MOE)(RS-2023-00218873).
How to cite: Jeong, Y. and Byun, K.: Shifts in Extreme Events over South Korea under Climate Change: An Analysis Using Extreme Climate Indices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9578, https://doi.org/10.5194/egusphere-egu25-9578, 2025.
Intensity-duration-frequency (IDF) curves based on high temporal resolutions are critical for applications within urban hydrology. However, such IDF curves rely on national rain gauge networks with low spatial resolution, and the methods for producing them vary from country to country. Recent advancements in the availability of rainfall data across Europe create new opportunities for generating IDF curves at a continental scale. Our overarching aim is to develop a scalable Machine Learning method for generating IDF curves across Europe and make the results available to the public, especially users of the Scalgo Live platform.
Our initial step is to create a target dataset based on gauged rainfall data. For this purpose, we compiled a dataset of gauged sub-hourly rainfall records from five European countries (Denmark, Germany, Norway, Poland and Sweden). More data will be added as they become available. We constructed annual maximum (AM) series of rainfall intensities for 15 durations ranging from 15 minutes to 7 days and fitted Generalized Extreme Value (GEV) distributions to the data.
While the location and scale parameters of the GEV distributions showed consistent spatial patterns overall, the shape parameter was highly variable, likely due to sampling uncertainty arising from the limited number of extreme observations in the tail of the distribution. The analysis revealed significant temporal non-stationarity in approximately 5% of the AM series and indicated systematic differences in the location parameter along the Danish-German border.
Future work will use the created target dataset to identify and develop a Machine Learning model that uses geographical and climatological covariates from publicly available datasets to predict the geographical variation of IDF parameters across Europe, enabling the generation of design rainfall in both gauged and ungauged areas.
How to cite: Hansen, S. S., Lerer, S. M., Löwe, R., Sørup, H. J. D., Hansen, J. T., and Mikkelsen, P. S.: Using sub-hourly data for estimating the frequency and intensity of extreme rainfall events across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8095, https://doi.org/10.5194/egusphere-egu25-8095, 2025.
Flooding is one of the most frequent natural disasters causing significant damage to natural and built environments. Ever increasing flood risk due to increase in urbanization and climatic change requires effective and efficient flood inundation mapping techniques to be used within global flood models. In this study, we evaluated multiple elevation-based hydrogeomorphic inundation models over the Eastern United States using high resolution digital elevation model (10meter). The inundation models we tested include the Hight Above Drainage Methodology (HAND), Relative Elevation model (REM), Geomorphic Flood Index (GFI) and a hybrid model between HAND and REM. We utilized the results of a hydrodynamic model as reference. Our results indicated that GFI methodology performs better compared to other methods, however requires calibration of three parameters for implementation, as opposed to one parameter for other models.
How to cite: Yilmaz, K. K., Sassi, M., Zanardo, S., Tillmanns, S., and Hilberts, A.: Evaluation of Multiple Geomorphic Flood Inundation Mapping Techniques over the Eastern United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19781, https://doi.org/10.5194/egusphere-egu25-19781, 2025.
The changing climate and evolving watershed conditions pose significant challenges to the safety of flood control structures. Assessing the current safety of these structures, originally designed using limited hydrological records from the pre-climate change era, may result in inaccurate risk assessments and mitigation strategies. Additionally, traditional design methods often relied on classical frequency analysis, examining flood characteristics from a univariate perspective. This approach overlooks the multivariate nature of floods, where mutually correlated characteristics such as peak flow, volume, duration, and shape play crucial roles. Therefore, multivariate frequency analysis and the examination of joint distribution probabilities are essential to accurately reassess the risks associated with reservoir safety.
This study presents a framework that has recently been proposed in Slovakia to design new and re-evaluate safety of old reservoirs. The framework respects and describes the dependence structures among the flood peaks, volumes, and durations of observed and synthetic control flood hydrographs. The probabilistic nature of the framework lies in the fact that rather than examining the safety based on a single control flood wave, it allows to generate a set of control flood waves with associated probabilistic parameters. The seasonality of flood generation is respected by separate analyses of floods in the summer and winter seasons for which a representative dimensionless shape of the flood hydrograph is derived from a set of flood hydrographs separated from the historical records. The framework consists of five key steps: (1) separation of observed hydrographs, (2) analysis of flood characteristics and their dependencies, (3) modelling marginal distributions, (4) applying a copula-based approach for joint distribution modelling of flood peaks, volumes, and durations, and (5) constructing synthetic flood hydrographs. This offers a diverse range of control waves for assessing the safety of water structures under extreme conditions, utilizing a probabilistic and process-based framework in typical failure risk scenarios.
This multivariate probabilistic framework was tested on a case study of the Liptovská Mara reservoir in the watershed of the Váh river in Slovakia, revealing significant seasonal differences. Winter floods exhibited longer durations and larger volumes, whereas summer floods were characterized by shorter durations, smaller volumes, and higher peak flows.
Acknowledgements
This work was supported by the Slovak Research and Development Agency, under the contract No. APVV-23-0332; APVV-20-0374, and the VEGA grant agency under contract No. VEGA 1/0577/23; VEGA 1/0657/25.
How to cite: Liová, A., Výleta, R., Valent, P., Bacigál, T., Hlavčová, K., Kohnová, S., Danáčová, M., Danáčová, Z., Jeneiová, K., Blaškovičová, L., Poórová, J., and Szolgay, J.: A multivariate probabilistic framework for estimating control flood hydrographs for reservoir safety re-evaluation in Slovakia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8955, https://doi.org/10.5194/egusphere-egu25-8955, 2025.
Parametric solutions can close the insurance gap while providing a protection for many developing nations in the world experiencing losses from natural catastrophes. The process generally involves real-time data analysis of environmental variables to verify the intensity of the event and if it passes or not the threshold specified in the policy. Despite the different benefits, developing effective policies is still very challenging due to regional variations in the parameters and the scarcity of high-quality and accessible data. This applies particularly to floods, which are also very difficult to model accurately given their complex nature.
In this study it is shown how GallagherRe has faced these challenges in developing a flood solution for the vulnerable population in Laos. The workflow developed relies on the Mekong River Commission river water level gauge data, which is assessed for quality in reconstructing selected historical events. To evaluate their intensity, a Generalized Pareto Distribution is fitted to the statistically independent extreme values extracted from the data for return period estimation, enhanced through Monte Carlo simulation. The information is then used to identify an equivalent flood extent derived from third party hazard maps for the catchments assigned to the selected gauge stations through an event agnostic approach. The reconstructed extent is finally intersected with the input risk to get an estimate of the vulnerable population affected.
The quality control of the gauge stations data identified that, due to a change in water level regime caused by anthropogenic events such as upstream dam regulation, only 16 out of the 28 available gauges can be used to support the parametric scheme. The limited catchments coverage determined for the valid gauges still allowed a significant portion of the risk to be captured in the hazard maps. In fact, most of the selected events resulted to be driven by the main Mekong River and its major tributaries, areas with both good valid gauge coverage and high population density. Despite this gap, it is also shown a positive correlation of increase in estimation with increasing size of event.
How to cite: Kwan, H. C., Scudeler, C., Felce, G., and Nagpal, H.: Parametric Flood modelling for the vulnerable population in Laos , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21728, https://doi.org/10.5194/egusphere-egu25-21728, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot A
EGU25-4786 | ECS | Posters virtual | VPS10
Revising probable maximum precipitation (PMP) estimates under changing climateThu, 01 May, 14:00–15:45 (CEST) | vPA.19
Probable Maximum Precipitation (PMP) is a key input in the design and risk assessment of critical infrastructures such as large dams and nuclear power plants. Traditionally, PMP is computed as a fixed upper bound of the precipitation assuming a stationary climate. However, due to climate change, the stationarity assumption may not remain valid in the future. Limited attempts have been made in the past to develop methods for estimating PMP by accounting for non-stationarity in the related hydroclimatic variables. In view of shortcomings associated with those methods, three new nonstationary models are proposed and their potential in determining PMP in a changing climate is illustrated through application to three major flood-prone river basins in India. In this analysis, historical records of precipitation, surface temperature and relative humidity, and their future projections corresponding to eleven CMIP-6 SSPs (Coupled Model Intercomparison Project-6 Shared Socio-economic Pathways) were utilized. The results indicate that PMP estimates obtained using the proposed nonstationary models are significantly higher than those obtained from their underlying conventional stationary model, especially for high-emission scenarios in the near future. The results obtained from this study could be utilized to update historical PMP values and to determine the increase in risk associated with the corresponding probable maximum flood.
How to cite: Bhatt, J. and Srinivas, V. V.: Revising probable maximum precipitation (PMP) estimates under changing climate , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4786, https://doi.org/10.5194/egusphere-egu25-4786, 2025.
