How to cite: Serre-Combe, C., Meyer, N., Opitz, T., and Toulemonde, G.: Spatio-temporal modeling of urban extreme rainfall events at high resolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6464, https://doi.org/10.5194/egusphere-egu25-6464, 2025.
Flood hazard assessment and mapping often rely on event-based approaches that assume uniform return periods for peak flows across an entire watershed. However, this simplification neglects the spatial and temporal heterogeneity intrinsic to flood events, potentially leading to inaccuracies in hazard estimation and, consequently, risk assessment. While many recent studies applying multivariate extreme value models focus on large-scale systems, this research applies the Heffernan and Tawn (HT) multivariate conditional exceedance model at the basin scale, using the Lambro River in Northern Italy as a test case.
The used hydrometric data required careful preprocessing to address gaps due to gauge malfunctioning or the lack of an appropriate rating curve to convert measured depths into flow rates. Missing data were handled using the Multiple Imputation by Chained Equations (MICE) method. This approach iteratively models missing values by leveraging relationships among variables, ensuring that the imputed data preserves the underlying structure and variability of the original dataset.
The Heffernan and Tawn (HT) multivariate conditional exceedance model was used to analyze the spatio-temporal dependencies of extreme flow rate values. The HT model characterizes the joint behavior of variables by conditioning the distribution of one variable on the exceedance of a high threshold by another, allowing the realistic modeling of flood scenarios. After the dependence structure was determined, Monte Carlo simulations were employed to generate synthetic events based on the estimated model’s parameters, producing a comprehensive set of scenarios that account for the spatial heterogeneity and temporal variability in extreme flows. The synthetic event generation thus captured the intricate dependencies between peak flows across locations, enabling the synthetic events to reflect realistic flood scenarios. By focusing on a small-scale rather than a regional or continental one referred to in prior applications of this method, this work aims at improving hazard assessment tools at the basin level.
In order to exploit the generated events in hazard assessment, one needs to (i) develop an approach to obtain the multivariate probabilities of occurrence for the generated scenarios, which remains a challenging and unresolved task, (ii) execute multiple hydrodynamic simulations across the range of generated scenarios, and (iii) statistically synthesize the simulation results.
How to cite: Rotaru, A. M. and Radice, A.: Accounting for Spatio-Temporal Dependencies in Flood Hazard Assessment at the Basin Scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6532, https://doi.org/10.5194/egusphere-egu25-6532, 2025.
River floods that exceed historical records often come as a surprise, causing widespread damage and disruption. To enhance disaster preparedness, it is essential to estimate exceptional flood scenarios that surpass past observations. There exist a number of methods such as stochastic storm transpositions, storylines, and downward counterfactuals to explore the space of extreme floods. The perfect storm concept uses unusual combinations of causative factors to generate extreme but plausible flood scenarios. We construct synthetic floods by mixing past severe rainfall events with observed antecedent catchment states from other floods or extreme catchment states but without flood occurrence. In this study, we apply this concept to develop exceptional flood scenarios by using the meso-scale hydrological model mHM driven by 70 years of meteorological data across Germany.
Our findings indicate that plausible perfect storm scenarios, respecting flood seasonality, can produce exceptional floods exceeding those observed in the past decades. Shifting rainfall to wetter soil conditions amplifies flood severity significantly, with some cases experiencing flooding up to seven times more severe compared to the original events. Even minor temporal shifts in rainfall, such as one month earlier or later, can drastically increase flood magnitudes, highlighting the significant impact of the temporal alignment of catchment state and rainfall events on flood severity. The perfect storm approach provides a practical means for identifying and communicating plausible and intuitive extreme scenarios with low probability. By integrating this method into flood risk management, planners and policymakers can better anticipate and prepare for the impacts of unprecedented flood events, reducing negative surprises.
How to cite: Han, L., Merz, B., Nguyen, V. D., Guse, B., Samaniego, L., Schröter, K., and Vorogushyn, S.: Floods we could have faced: exploring exceptional flooding using perfect storm concept, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8953, https://doi.org/10.5194/egusphere-egu25-8953, 2025.
River flooding impacts more people globally than any other natural disaster. River confluences are key nodes of a river network, characterized by complex hydrodynamic conditions. Hence, a general framework to investigate the sensitivity of confluences to extreme peak flows upstream, climate, and geomorphological characteristics is of great importance. Currently, a systematic large-scale investigation of peak flows of main river upstream and tributary and their interactions is missing in the literature. Here we analyse upstream and downstream peak flows and their relative occurrence in 153 catchments (51 confluences) across the world. The results indicate that the time lag between the upstream and downstream discharge can be explained by their seasonality of the climate zone. This leads to different patterns of flood events in confluences, which can occur even when upstream discharges are not hazardous per se. The probabilistic characterization of the co-occurrence of peak discharge downstream of the confluence for different conditions upstream shows that the co-occurrence of extreme discharges upstream and downstream is not the most likely scenario, whilst the probability of peak discharge downstream with corresponding moderate discharges upstream cannot be neglected. These outcomes provide a general framework for improving the flood resilience in the proximity of the confluence.
How to cite: Diakomopoulos, F., Ragno, E., Hrachowitz, M., and Stancanelli, L. M.: A probabilistic analysis of compound flooding in river confluences under different hydroclimatic and geospatial conditions., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9103, https://doi.org/10.5194/egusphere-egu25-9103, 2025.
The flooding in the Valencia region on October 29 was unprecedented. It caused over 200 deaths and impacted an area far beyond the boundaries of the official 500-year flood maps. Its vast scale has raised many questions about the predictability of such events. In many ways, this disaster is reminiscent of the 1999 Vargas tragedy in Venezuela[1].
In this study, we analyze the return levels of rainfall recorded by several pluviometers in the affected area to assess the likelihood of such an event occurring and how this probability changes after the event has been observed. We apply multiple techniques to evaluate their stability and robustness in estimating return levels. These techniques include: maximum likelihood fitting of extreme value distributions, regional frequency analysis, L-moments distribution fitting, Bayesian techniques for station data, Bayesian hierarchical models for the region, and stochastic generation.
Our preliminary results indicate that the magnitude of the event far exceeded the usual design values, which may explain the extent of the destruction in the affected area. However, the stability of predictions varies significantly across methods. Our findings highlight that representing design values as distributions rather than single values provides a clearer understanding of the uncertainties inherent in extreme value modeling.
Additionally, some of our results suggest that such an event alters expectations for future extreme events, necessitating a reassessment of risk levels for infrastructure across the entire region.
References:
[1] Coles, S., Pericchi, L., 2003. Anticipating catastrophes through extreme value modelling. Journal of the Royal Statistical Society Series C-Applied Statistics 52, 405–416.
How to cite: del Jesus, M., Navas, S., and Urrea, D.: An analysis of return levels of Valencia's 2024 extreme rainfall, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10317, https://doi.org/10.5194/egusphere-egu25-10317, 2025.
Extreme floods are caused by special meteorological conditions matching critical space-time scales of flood generation processes in a catchment. Fortunately for most of the floods these conditions do not meet. Objective of this study is to investigate how big could have historical floods become under “optimal” rainfall conditions and which are the main factors driving flood maximization. For that, observed storms are stochastically modified. First the intensities are amplified, then the spatial patterns are changed and finally both conditions are varied together. The rainfall intensities are modified with moisture maximisation considering the observed saturation deficit. The spatial patterns are changed by conditional rainfall simulation considering temporal correlation and advection. The simulated rainfall realisations are then used as input for a rainfall-runoff model to simulate corresponding floods. This case study uses data from the Mulde river basin in Germany and applies the methodology to a set of selected large flood events. The results show that observed events could have been much worse with average amplification factors of 2.9 for rainfall and of 4.6 for peak flow for the worst-case scenario of stationary storms. The developed method could also be used as alternative for the estimation of probable maximum precipitation and probable maximum floods.
How to cite: Haberlandt, U., Thiele, L.-B., and Sharma, A.: Maximisation potential of observed floods using conditional rainfall simulation and moisture maximisation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10407, https://doi.org/10.5194/egusphere-egu25-10407, 2025.
Extreme weather and climate events such as heavy precipitation, drought, heat waves and strong winds can cause extensive damage to society in terms of human lives and financial losses. As climate changes, it is important to understand how the spatial distribution of extreme weather events may change as a result.
Most spatial statistical models measure spatial dependence between variables at different spatial locations directly, typically by their distance separation or via a Markov process. This study differs from previous research by examining the spatial aspect of essential field quantities, conditioned on the occurrence of extreme events somewhere in the field. Although some spatial fields may not encounter any extreme events over time, applying the Positive Extreme Field (PEF) concept (Kholodovsky and Liang (2021)) suggests that one or more extreme regions will exist. We refer to this modeling technique as the Propinquity (PQ) modeling framework.
Two different statistical approaches are utilized to model extreme events. First, the traditional univariate generalized Pareto (GP) model is applied to individual grid cells with quantile-based thresholds. Second, rather than considering extreme values at individual locations and their temporal dependence, we consider an overall spatial field conditioned on being extreme by utilizing the Heffernan and Tawn model (2004) with PEFs from the STTC algorithm.
We apply these models to an observed precipitation dataset over CONUS and compare resulting trends in probabilities and return levels. The findings highlight the risks of aggregating univariate model results in space and emphasize the need to account for the connectivity between individual grid cells when calculating historical trends.
This work introduces a novel statistical methodology that enhances our understanding of added value —specifically, by conditioning on PEFs and accounting for the connectivity between individual grid cells—through the multivariate PQ modeling framework, which enables analysis of spatio-temporal dependence for extreme fields that traditional univariate approaches do not capture.
How to cite: Kholodovsky, V., Gilleland, E., and Liang, X.-Z.: Comparing a spatial propinquity extreme-value model with a simple univariate generalized Pareto approach for trends in extreme precipitation. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12517, https://doi.org/10.5194/egusphere-egu25-12517, 2025.
It is now well understood that anthropogenic induced global warming is increasing extreme rainfalls, with the more extreme the rainfall, the greater the intensification. This in turn is increasing the magnitude of rare floods. For floods with annual exceedance probabilities rarer than 1 in 20, the intensification of rainfall offsets any decreases in soil moisture. Less well understood, however, is the changing impact of spatial and temporal patterns of extreme rainfall on flooding under a warming climate. The spatial and temporal distribution of rainfalls during storm events has a significant influence on runoff volumes (and hence water availability) and on flood peaks. Hence, robust datasets are required to model hydrologic risk with changes in storm spatial and temporal patterns.
To this end, we have developed Australia-Wide Extreme Storms Database (or AWESD) which characterises storm patterns with high spatial (12km) and temporal (hourly) resolution for hydrologic risk assessments. Whilst the record length for such high-resolution data is currently 30 years, the availability of information at a high resolution over large homogeneous regions allows the trading of space for time, which has the potential to provide equivalent independent record lengths that are much longer than 30 years. To develop such a database, we first identify and track storms using two data sets: a low resolution (daily) gridded dataset based on observations, and a higher resolution (hourly) reanalysis dataset. Identified storms are then filtered to ensure they are independent in both space and time. Storms identified in the high-resolution reanalysis dataset are checked for consistency with the observation-based data set to ensure a grounding in reality.
Having developed the database, we then created a software for storm selection. Storms are selected based on an input catchment location plus a prespecified buffer region and within a range of prespecified ratios of catchment size. Storms are then transposed to the catchment centre. The final storms selection can then be formatted to facilitate input to event-based flood modelling software.
The development of the extreme storms database and storm selection software facilitates the undertaking of hydrologic risk assessments as storms may be sampled on depth/rarity, spatial homogeneity and temporal homogeneity. For example, it can be used to investigate how spatial and temporal patterns of rainfall may vary with event severity, and this could be used to inform estimates of dam failure risks. Furthermore, it can be used for climate impact assessments by sampling storms based on characteristics associated with a warmer climate e.g. higher depths and shifting spatio-temporal pattern distributions. With this storm database we believe it will enhance hydrological risk assessments performed for both present and future climate scenarios and deepen understanding of the role of spatio-temporal distributions on extremes.
How to cite: Dykman, C., Kim, Y., Nathan, R., Sharma, A., and Wasko, C.: Development and Application of Australia-Wide Extreme Storms Database for Hydrologic Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14313, https://doi.org/10.5194/egusphere-egu25-14313, 2025.
Design rainfall estimation is critical for hydrological infrastructure planning and risk management. Traditional methods often rely solely on rainfall intensity, overlooking essential event-scale characteristics like spatial extent, duration, and precipitation volume, which play an important role in rainfall-runoff modeling. To address these limitations, this study adopts a multivariate approach to incorporate additional physical characteristics of rainfall events and enhance design rainfall estimation.
A key preliminary step involved the construction of a comprehensive rainfall event dataset for Tuscany, Italy, using high-resolution time series data from 270 rain gauges (1999–2024). To shift from point-based intensity data to event-scale analysis, specific criteria were defined to identify individual rainfall events. This process involved grouping measurements based on their spatial and temporal proximity and applying interpolation techniques to derive a unified set of physical characteristics for each event. The resulting dataset includes attributes such as total volume, duration, and spatial extent, offering a holistic representation of each rainfall event.
Two distinct approaches were employed to model the relationships between event characteristics and estimate return periods for extreme events. The first approach employs Factor Analysis to reduce the dimensionality of the dataset by identifying independent latent variables that capture the linear relationships within the features. This method allows for the separate analysis of the marginal distribution using conventional univariate Peak Over Threshold (POT) techniques, though it sacrifices direct physical interpretability. The second approach utilizes copulas to model dependencies among the original event characteristics, providing a flexible and physically meaningful framework for joint distribution analysis.
This work contributes to the ongoing research by providing a robust framework for multivariate analysis of rainfall events, offering more informative design rainfall estimates to support flood modeling and risk management.
This study was conducted within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005)
How to cite: Di Bacco, M., Manzella, F., Mazzanti, B., and Castelli, F.: Multivariate Analysis of Extreme Rainfall Events in Tuscany: Comparing Factor Analysis and Copula-Based Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18618, https://doi.org/10.5194/egusphere-egu25-18618, 2025.
Severe climate events are becoming more frequent, leading to many fatalities, significant economic damage and disruptions to vital infrastructure. As a result, accurately estimating the frequency and potential consequences of widespread extreme events has become a critical need. However, the limited availability of observations of extreme events poses a major challenge for impact studies, and even large sets of climate simulations often lack sufficient extreme or record-breaking events for thorough analysis. In contrast, weather generators adapted to extreme observations can efficiently produce a large number of plausible extreme events, even those with unprecedented intensity levels.
Using fundamental principles from spatial extreme-value theory, we adapt traditional Fourier-based phase-randomisation to specifically generate high-resolution synthetic datasets of rare extreme events. The key feature is that the stochastically generated datasets exhibit the same spatial tail dependence as the observed extreme events. Compared to other existing methods for modelling spatial extremes, our approach is distinguished by speed, easy implementation and scalability to higher dimensions.
Using high-resolution datasets for precipitation and temperature, we show that our algorithm produces realistic spatial patterns of extreme events. We successfully generated datasets with 10,000 grid points, and this number can be easily increased. Given the need for high-resolution climate data in many impact models, our algorithm is particularly useful for robust impact and vulnerability assessments.
References
- Van de Vyver, H. (2024) Fast generation of high-dimensional spatial extremes, Weather Clim. Extrem. 46, 100732. https://doi.org/10.1016/j.wace.2024.100732.
How to cite: Van de Vyver, H.: Fast generation of widespread extreme events based on extreme-value theory, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1577, https://doi.org/10.5194/egusphere-egu25-1577, 2025.
Hydrological extremes pose a serious threat to critical functions and services provided by terrestrial ecosystems. Anticipated increases in frequency and severity of droughts due to climate change is expected to negatively impact ecosystem functioning and vegetation health. The ability of vegetation to recover after a drought episode is an important metric of the drought impact. Wide-spread vegetation drought impacts can result in a regional decrease in ecosystem resilience and an overall decline in ecosystem health. Analyzing the post-drought recovery characteristics of vegetation and it spatial connectiveness provides vital information on the vulnerability to future droughts and its ability to deal with reoccurring drought events or multi-year drought events.
However, the spatial dependence of post-drought vegetation recovery i.e. the extent to which events co-occur at multiple locations simultaneously, is largely unknown and unstudied. In our research, we identify the spatial dependence of vegetation recovery after a drought using complex networks and event synchronization (ES). Thereby we aim to explain the underlying mechanisms and patterns which could potentially support recovery forecasting in the future.
Drought events are selected based monthly SPEI and EVI data, where an ecological drought event is defined as an EVI-anomaly coinciding with a meteorological drought. Based on these events, we create networks of drought event (recovery) co-occurrences. With the use of ES the spatial dependence of different stages of vegetation recovery is quantified. Additionally, regions with similar recovery capacity are evaluated to what degree their recovery responses can be explained by temporal or spatial factors like hydro-meteorological and geographical characteristics.
Our first results show that both geographical, climatological and hydro-meteorological factors are significantly different between regions with similar recovery behaviour. This highlights the relevance of using both temporal and spatial factors when studying the resilience of ecosystems after drought impact.
How to cite: Verhoeve, S., Hauswirth, S., de Jong, S., and Wanders, N.: Spatial dependence of vegetation recovery after drought events and the spatiotemporal characteristics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3243, https://doi.org/10.5194/egusphere-egu25-3243, 2025.
The need for long-term synthetic sub-daily rainfall time series is crucial in various hydrological applications, particularly in flood frequency analysis. Traditional sub-daily rainfall simulation models rely on high time-resolution data, typically spanning only 20–30 years, which is insufficient for generating the long synthetic time series required for high return period design value estimation. In contrast, longer datasets of daily rainfall records and annual maximum values are more widely available, often covering 50–80 years. These datasets underpin the derivation of Intensity-Duration-Frequency (IDF) curves, a cornerstone of current hydrological practice.
This study introduces an innovative framework for simulating sub-daily rainfall time series using only daily rainfall records and IDF curves, thus eliminating the need for sub-daily observational data. The approach integrates a daily rainfall simulation model, Complete Stochastic Modelling Solution, calibrated with observed daily data, with a multifractal disaggregation scheme informed by IDF curves. The resulting framework offers a robust and parsimonious solution for generating sub-daily rainfall data.
By leveraging readily available datasets, this method expands the applicability of sub-daily rainfall simulations to a broader range of hydrological and climate modeling contexts, providing a valuable tool for advancing flood frequency analysis and related applications.
How to cite: Grimaldi, S., Volpi, E., Langousis, A., Deidda, R., Papalexiou, S. M., Perdios, A., and Cappelli, F.: Overcoming Data Limitations in Sub-Daily Rainfall Simulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3564, https://doi.org/10.5194/egusphere-egu25-3564, 2025.
Extreme rainfalls are important hydrometeorological variables for water resources management, flood mitigation, and soil conservation in Iran. This study examined annual and monthly maximum 24-hour rainfall from 135 stations across Iran, focusing on trends, frequency distributions and stochastic characteristics to investigate spatial and temporal patterns. Although a few stations exhibit statistically significant trends, most western and northern regions show increasing trends, while decreasing trends dominate the central and eastern semi-arid areas. Frequency analysis identified the Generalized Logistic distribution as the most prevalent distribution for extreme rainfall in Iran, with no clear spatial pattern in distribution type. In addition, spatioal analysis of L-moment statistics revealed high L-coefficients of variation in arid and semi-arid regions, while skewness and kurtosis did not show distinct spatial patterns. Lag-1 and Lag-12 autocorrelation coefficients of monthly extreme rainfall were also examined, revealing weak temporal memory and seasonal autocorrelation for most stations. Seasonal autocorrelation was more pronounced in the humid and semi-humid western and northern regions compared to the arid and semi-arid regions of Iran. These results highlight significant spatial heterogeneity in extreme rainfall patterns and underscore the challenges of predicting extreme rainfall events due to their low temporal predictability and high uncertainty. The results emphasize the need for robust hazard and risk management strategies to address rainstorm- and flood-related risks across Iran.
How to cite: Torabi Haghighi, A., Gohari, A., Mohit Isfahani, P., Modarres, R., and Miao, C.: Spatial and temporal pattern of rainfall extremes in Iran, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3425, https://doi.org/10.5194/egusphere-egu25-3425, 2025.
Compared with univariate extreme climate events, such as extreme precipitation, droughts, cold spells, and heat waves, Compound Extreme Events (CEEs) have greater impacts on human activities and economic development. Gaining a deeper insight into the topological structure and evolutionary direction of CEEs is crucial for understanding their potential responses to altered thermodynamics and dynamics in a warming climate. Graph theory-based complex networks can effectively represent the relationships among various elements of complex dynamical systems, such as the atmosphere. They are thus used to analyze the directionality and topological structure of CEEs over a 60-year period across mainland China. Specifically, the CEEs are constructed by the combinations of univariate temperature and precipitation extreme events, with each univariate extreme event identified by fixed percentiles of the daily precipitation and temperature data. Our results reveal important structural and dynamical information about the topology of the CEEs and improve the understanding of the dominant meteorological patterns. The initiation and propagation of CEEs from source to sink zones are discerned, and their topological structure and spatial dynamics are influenced by topography, wind patterns, and moisture sources.
How to cite: Fei, J., Zhang, X., Li, C., and Hao, F.: Compound Extreme Events Propagation Over China: A Complex Network Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4059, https://doi.org/10.5194/egusphere-egu25-4059, 2025.
In the context of global warming, daily precipitation extremes are becoming increasingly frequent and severe, further exacerbating the uneven distribution of daily precipitation throughout the year. The number of Wettest Days that contribute to 50% of the annual precipitation (WD50) is a key metric for understanding how annual precipitation is disproportionately driven by a small number of days, with significant implications for climate science and water resource management. Despite its importance, there remains limited research on WD50, particularly from the vantage point of satellites. Therefore, our study leverages NASA’s Integrated Multi-satellitE Retrievals for Global precipitation measurement mission (IMERG) to examine global patterns in WD50. IMERG data reveal substantial variability in WD50 across reference climate regions worldwide, with lower WD50 values in dry areas and higher values in wetter ones. Comparison with over 31000 rain gauges in the Global Historical Climatology Network (GHCN) confirms IMERG’s alignment with ground data at specific locations (R2 between 0.49 to 0.68). This analysis demonstrates IMERG’s capability to capture the wettest days as key contributors to annual precipitation. Furthermore, our research provides new insights into the heterogeneous distribution (spatially) in precipitation unevenness (temporally), which is essential for understanding regional rainfall patterns and their intensification in response to climate change.
How to cite: Goffin, B. and Lakshmi, V.: Assessing how Annual Precipitation is Driven by the Wettest Days using IMERG Earth Observation Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11590, https://doi.org/10.5194/egusphere-egu25-11590, 2025.
Understanding the scale-dependent behavior of extreme precipitation in mountainous basins is critical for improving effective adaptation strategies to rising flood risks. This study investigates the future changes in sub-daily catchment scale extreme precipitation across the Great Alpine Region. In particular, we examine how information about the projected changes of sub-daily point design precipitation can be transferred to projected changes of catchment-scale precipitation with the same return period.
Projections are derived from a 9-member ensemble of convection-permitting climate models (CPMs) provided by the CORDEX-FPS Convection project. The dataset spans historical (1990–1999) and far-future (2090–2099) periods under the high-emissions scenario (RCP8.5), with precipitation outputs remapped to a 3 km spatial resolution and a 1-hour temporal resolution. To analyze extremes, we apply the Simplified Metastatistical Extreme Value (SMEV) framework, a robust non-asymptotic statistical method well-suited for short data records.
The spatial analysis focuses on mean areal precipitation extremes, computed over various moving average window sizes, with the largest block encompassing an area of approximately 4000 km² (21 × 21 grid cells). Changes in 3-km grid design precipitation are translated to catchment-scale design events by quantifying changes in Areal Reduction Factor (ARF). We calculate the Areal Reduction Factor (ARF) for the window sizes across different durations and return periods, enabling us to quantify the scaling relationships between 3 km grid and areal precipitation extremes. By examining the dependence of ARF on duration and return periods under future climate conditions, we identify potential shifts in the spatial structure and intensity of extreme precipitation events. Our study underscores the importance of using high-resolution ensemble modeling to capture the complex interplay between spatial variability and extreme precipitation, and contributes to addressing the challenges posed by changing precipitation extremes in mountainous regions.
How to cite: Akbary, R., Marani, M., Dallan, E., Marra, F., and Borga, M.: Future changes in sub-daily catchment scale extreme precipitation in the Great Alpine Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12970, https://doi.org/10.5194/egusphere-egu25-12970, 2025.
Estimating extreme hydrometeorological events, such as storm surges or extreme precipitation , is crucial for effective flood risk management, particularly in poorly gauged or ungauged regions. As climate change intensifies, these events are expected to increase in frequency and severity, making reliable predictions even more vital for vulnerable areas. Traditional methods, such as asymptotic extreme value distributions, often face significant uncertainties when dealing with short observational records, which are common in many regions. This results in high uncertainties in extreme event prediction, thereby hindering effective preparedness and response strategies.
In this study we introduce an approach to hydrometeorological extremes that combines the Metastatistical Extreme Value Distribution (MEVD) and a flexible regionalization technique, aiming to overcome the limitations set by data scarcity in traditional at-site analysis methods. Unlike asymptotic methods, which uses only a small subset of the available observations, the MEVD method leverages the information contained in all observed events to infer the probability distribution of annual maxima. This approach is particularly beneficial when the data records are scarce, allowing for more accurate estimation of very rare events. Uncertainties can be further reduced by exploiting spatial information to compensate for the lack of information in time. The flexible regionalization approach proposed, unlike traditional regionalization methods, does not impose rigidly defined regions composed of statically homogeneous sites with predefined spatial boundaries. Rather it accounts for the observational information contained in the vicinity of the site where the estimation is being carried out by introducing a weight according to a similarity criterion. This feature allows for a seamless integration of data across varying spatial and temporal domains and a better representation of the continuous nature of hydrometeorological processes.
In this contribution the performance of the flexible MEVD-based regional approach is appliedcompared with that of state-of-the-art regionalization approaches.
How to cite: Devò, P., Wahl, T., and Marani, M.: Statistical modeling of hydrometeorological events in poorly gauged coastal areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13172, https://doi.org/10.5194/egusphere-egu25-13172, 2025.
Residuals from hydrological models are critical for evaluating model performance, improving predictive accuracy, and deepening the understanding of hydrological processes. Enhancing predictive methods is especially crucial for capturing extreme events, which have significant implications for risk management and planning. These residuals, however, are influenced by model structures, preprocessing methods, and catchment characteristics. This study addresses these complexities by systematically analyzing the statistical properties of residuals under various transformations and preprocessing treatments. The analysis spans a diverse dataset of catchments across a broad range of hydroclimatic conditions, with residuals generated from simulations of multiple hydrological models, ensuring both the generality and robustness of the findings.
Key aspects of the research include the evaluation of residual properties under transformations, such as log-transformation, and the role of preprocessing steps. Through this approach, the study provides a more consistent framework for assessing variability, skewness, kurtosis, autocorrelation, and dependency structures in residuals. Additionally, the analysis encompasses heteroskedasticity and tail dependencies, capturing the nuances of residual behavior across different contexts.
The dataset’s extent is a defining strength of this study. By involving simulations from a wide range of hydrological models (78 configurations) and including catchments with varying climatic and physical characteristics (more than 400 basins in the United States, ranging from dry to wet climates), the research delivers insights that are widely applicable to diverse hydrological conditions. This breadth ensures that findings are relevant for both theoretical advancements and practical applications, offering guidance to researchers and practitioners working with different modeling systems and catchment types.
A central result highlights the transformative impact of removing seasonality from residuals. De-seasonalization not only stabilizes key residual properties but also reduces variability across models, facilitating a clearer evaluation of model performance and error structures, underscoring the importance of standardizing preprocessing techniques in hydrological modeling, as it enables more robust and interpretable diagnostic frameworks. These aspects will be discussed in depth during the EGU presentation, with a focus on their relevance and practical implications.
How to cite: Lombardo, L., Papalexiou, S. M., Clark, M., Thébault, C., and Viglione, A.: Properties of hydrological model residuals: a large sample study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13466, https://doi.org/10.5194/egusphere-egu25-13466, 2025.
Hydrological droughts are often viewed through the immediate lens of atmospheric droughts, driven by precipitation deficits and evaporative demand. However, these droughts can be exacerbated by the long-term impacts of baseflow changes, which alter groundwater-fed streamflow critical for sustaining hydrological systems during prolonged dry periods. This study employs a global dataset of 7,138 catchments and the PCMCI+ causal discovery algorithm to unravel the spatiotemporal drivers of baseflow changes and their relationship with hydrological drought severity. We identify key climatic controls—precipitation, evaporative demand, and snow fraction—and quantify their influence across diverse climate zones. Precipitation emerges as the dominant driver globally (58.3% of catchments), while evaporative demand and snow fraction govern baseflows in tropical and polar regions, respectively. By mapping concurrent spatial occurrence in baseflow and hydrological drought, we delineate zones of critical risk where these processes overlap, exacerbating vulnerability to extremes. This study advances our understanding of spatiotemporal extremes and offers insights for improving the modeling and management of compound hydroclimatic events under climate change.
How to cite: Zaerpour, M., Hatami, S., Ballarin, A. S., Papalexiou, S. M., Pietroniro, A., and Adamowski, J. F.: Understanding Drivers of Baseflow Changes and Their Role in Hydrological Droughts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13472, https://doi.org/10.5194/egusphere-egu25-13472, 2025.
Understanding how climate change affects precipitation patterns—spanning daily, seasonal, and extreme events—at the catchment scale is essential for assessing regional hydrological shifts and guiding water resource management. This study investigates future precipitation changes across eleven key Canadian catchments using 9-km downscaled simulations from CMIP6 under various Shared Socioeconomic Pathways (SSPs). Through detailed analysis of daily, seasonal, and extreme precipitation metrics, we reveal significant insights into future precipitation dynamics. The findings indicate substantial increases in daily precipitation, with northern and coastal regions showing the highest growth, particularly under the SSP5-8.5 scenario. Seasonal patterns reveal marked precipitation increases in spring and winter, with consistently elevated values in coastal and mountainous areas. Extreme precipitation events, including annual maxima and 95th and 99th percentiles, intensify notably under high-emission scenarios, with northern regions experiencing the most significant relative changes. These results emphasize the urgency of developing region-specific climate adaptation strategies to address emerging risks related to flooding, water resource management, and infrastructure resilience in the context of a changing climate.
How to cite: Abdelmoaty, H., Gavasso-Rita, Y., and Papalexiou, S.: Future Precipitation Trends Across Canadian Catchments: Insights from High-Resolution CMIP6 Downscaled Projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14173, https://doi.org/10.5194/egusphere-egu25-14173, 2025.
Regional frequency analysis (RFA) is a more reliable method for estimating hydrological quantities than at-site frequency analysis, particularly in countries like South Korea where the observation period for hydrological data is relatively short. The results of RFA vary depending on the classification of hydrologically homogeneous regions. With the increasing occurrence of extreme climate events due to climate change not only in South Korea but also globally, the validity of existing hydrologically homogeneous regions defined solely by historical rainfall data is now in question. Currently, South Korea’s flood estimation guidelines classify the country into 26 homogeneous regions based on hydrological data collected up to 2017, without considering the impacts of climate change. Therefore, it is necessary to evaluate whether the currently used Generalized Extreme Value (GEV) distribution remains appropriate by conducting a goodness-of-fit test after redefining hydrologically homogeneous regions. This study aims to reclassify South Korea's hydrologically homogeneous regions for rainfall regional frequency analysis using the up-to-date rainfall data and clustering analysis techniques. After collecting recent rainfall data, the data will be corrected using the Inverse Distance Weighting (IDW) method, followed by the reclassification of homogeneous regions through three clustering methods. The clustering methods to be applied include k-means, Self-Organizing Maps (SOM) based on artificial neural networks, and t-Distributed Stochastic Neighbor Embedding (t-SNE), a dimensionality reduction technique for high-dimensional data. The results of the homogeneous region classifications derived from each clustering method will be compared using measures of discordance(H) and heterogeneity(Di). This study is expected to provide insights into how climate change affects the classification of homogeneous regions in regional frequency analysis.
How to cite: Lee, G.-Y., Park, J., Jang, S., Kim, S., and Shin, J.-Y.: Investigation of Changes in Hydrologically Homogeneous Regions for Regional Frequency Analysis under Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14522, https://doi.org/10.5194/egusphere-egu25-14522, 2025.
Heatwaves pose significant risks to human health, agriculture, and environmental systems and thus have received substantial attention globally. However, the lack of a standardized definition, with thresholds varying in terms of duration, magnitude, and contributing variables, often complicates the evaluation of heatwave risks. Addressing this gap, this study proposes a copula-based framework for developing an Integrated Heat Index (IHI) that synergistically incorporates daily maximum temperature (Tmax) and daily minimum relative humidity (RHmin) to analyze heatwave variability across India. This study utilizes high-resolution (0.5° × 0.625°) data obtained from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), covering a period of 43 years (1981–2023) and focusing on the pre-monsoon (March-May) and monsoon (June–September) seasons. We used the Kolmogorov–Smirnov test to find the best marginal distributions for Tmax and RHmin. Eight different distributions were examined: Extreme Value, Generalized Extreme Value, Generalized Pareto, Logistic, Normal, Gamma, Lognormal, and Weibull. We used copula functions (Gumbel, Clayton, Frank, and Gaussian) to model joint dependencies and chose the best copula based on Akaike Information Criterion (AIC). In this study, a heatwave is characterized by its attributes, such as frequency (F), accumulated intensity (Icum), peak intensity (Ipeak), and duration (D). Although these characteristics of heatwaves are closely interconnected, they are often studied separately, especially over the Indian subcontinent. Here, we assess the joint return period of heatwaves over India using bivariate analysis, considering the combinations of D-Ipeak, D-Icum and Ipeak-Icum. This integrated approach offers a robust tool for assessing heatwave dynamics and provides critical insights into their spatial and temporal variability across India, facilitating improved risk assessment and management strategies for diverse stakeholders.
How to cite: Mohseni, U. and Rajendran, V.: A Copula Framework for the Development of an Integrated Heat Index and Joint Return Period Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18878, https://doi.org/10.5194/egusphere-egu25-18878, 2025.
Reliable estimates of extreme precipitation are fundamental for understanding, predicting, and mitigating natural disasters. However, the inference of extreme precipitation magnitudes at the global scale is severely constrained by the low and uneven density of direct rainfall observations. Satellite-based rainfall estimates offer a promising source of information to support extreme value analysis but are hindered by high estimation uncertainty and coarse spatial resolutions. The coarse scale of global datasets, with grid sizes typically ranging from 100 to 600 km², prevents direct comparisons with point-scale extreme value estimates because point and area-averaged statistics inherently differ by construction.
This study addresses this limitation by systematically applying a downscaling method for extreme-value statistics based on the theory of random fields and the Metastatistical Extreme Value Distribution (MEVD). We utilize a large dataset from approximately 200 rain gauges in Northeastern Italy and multiple satellite precipitation products, including IMERG, CMORPH, CHIRPS, SM2RAIN, MSWEP, and PERSIANN. Downscaling, based on the autocorrelation structure of the precipitation fields, is performed for each individual product on the grid cells corresponding to the available rain gauges.
We compare downscaled estimates of daily 50-year return period event magnitudes with those derived from rain gauge time series, for individual products as well as for central tendency statistics of the ensemble. Additionally, we quantify the frequency distribution of estimation errors associated with different products and with their ensemble.
This research was supported by the "raINfall exTremEs and their impacts: from the local to the National ScalE" (INTENSE) project, funded by the European Union - Next Generation EU in the framework of PRIN (Progetti di ricerca di Rilevante Interesse Nazionale) programme (grant 2022ZC2522).
MM was also supported by 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: Sanchez Pena, C. A., Marra, F., and Marani, M.: Estimates of Point Rainfall Extremes from Satellite Precipitation Products: Application and Testing in Northeastern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16975, https://doi.org/10.5194/egusphere-egu25-16975, 2025.
