In September 2023 storm Daniel struck the area of Thessaly, in the central part of Greece, causing extreme rainfall over four consecutive days. The aftereffects were devastating, as 17 people died, extensive damage -yet to be restored- was caused to infrastructure (including roads, bridges and the port basin of Volos) and the economic impact was also severe. This devastating disaster could have been limited if a reliable estimate of flood risk was available and efficient risk mitigation measures were adopted. To move a step forward towards such target, stochastic models serve as powerful tools for predicting floods and extreme rainfall incidents since they accurately simulate the inherent uncertainty that characterises natural processes like precipitation and river flows. In this work, we obtain historical precipitation data for the area of Thessaly and by applying the appropriate stochastic models and procedures we generate synthetic rainfall data. Then, by comparing the synthetic data to the historical, in stochastic terms, we test at what degree the stochastic models can effectively capture the variability of natural processes. The resulting synthetic data provide valuable insight in the likelihood of occurrence of extreme events, paving the way for incorporating stochastic tools in the development of flood early warning systems (FEWS). Furthermore, the application of stochastic models in extreme rainfall events will also guide the infrastructure design, in order to be resilient against extreme weather events, and will facilitate water resources management.

How to cite: Vrettou, S., Koutsoyiannis, D., Dimitriadis, P., Iliopoulou, T., and Montanari, A.: A stochastic approach on the extreme hydrological events: the case of Thessaly, Greece, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9253, https://doi.org/10.5194/egusphere-egu25-9253, 2025.

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application in Greece remains limited. We focus on applying machine learning models to create flood susceptibility maps for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. The study integrates topographical, hydrological, hydraulic, environmental and infrastructure data to train the models. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision-making for disaster preparedness in Thessaly.

How to cite: Tepetidis, N., Benekos, I., Iliopoulou, T., Dimitriadis, P., and Koutsoyiannis, D.: Applying machine learning models for flood susceptibility mapping in Thessaly, Greece, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5194, https://doi.org/10.5194/egusphere-egu25-5194, 2025.

This study investigates the impacts of climate change on extreme hydrometeorological events using an integrated modeling framework that couples the Weather Research and Forecasting (WRF) model with WRF-Hydro. WRF is a numerical weather prediction model that simulates a wide range of atmospheric phenomena, while WRF-Hydro is a physics-based hydrological modeling system that represents hydrological states and their spatiotemporal distributions and interactions. The primary advantage of this integrated approach is the consistent sharing of land surface and boundary conditions between atmospheric and hydrological simulations. This research focuses on Typhoon Hinnamnor, which brought record-breaking rainfall and severe flooding to South Korea in 2022. Multiple WRF simulations with various microphysics schemes are conducted to determine the optimal configuration for retrospective meteorological simulations. Hydrological simulations driven by both ground-based and WRF-generated forcings are analyzed to evaluate hydrological responses at multiple gauging stations along the main channel and local tributaries. Additionally, extreme hydrometeorological conditions under climate change scenarios, projected by the WRF and WRF-Hydro models, are estimated using key meteorological and hydrological variables, including typhoon trajectory, precipitation, pressure, wind speeds, soil moisture, and streamflow. The discussion highlights the advantages and challenges of the integrated modeling approach, as well as the impacts of climate change on hydrometeorological variables across different spatial and temporal scales. Furthermore, we explore strategies for assessing the combined effects of climate and land cover changes using a high-resolution, fully interactive modeling setup.

How to cite: Lee, Y., Kim, B., Hiraga, Y., and Noh, S. J.: Assessing the impacts of climate change on extreme hydrometeorological events using an integrated WRF and WRF-Hydro framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8232, https://doi.org/10.5194/egusphere-egu25-8232, 2025.

Extreme precipitation (EP) events have intensified in arid and semi-arid regions due to climate change. Understanding the causal mechanisms driving EP at the sub-seasonal timescale is crucial for improving prediction accuracy and extending forecast lead times. This study investigates EP characteristics in Northwest China (ASRNC) and reveals through a composite analysis that EP is associated with specific atmospheric circulation patterns, including anomalous upper-level thermal and lower-level dynamic conditions. Moisture transport from the Indian Ocean, South China Sea, Mediterranean, and North Atlantic fuels EP events in different regions of the ASRNC. To quantify causal relationships, the extended convergent cross-mapping (CCM) method is employed. CCM outperforms traditional correlation analysis in capturing time lags and directional causality between variables. External circulation factors, such as geopotential height, zonal/meridional winds, outgoing longwave radiation, and specific humidity, exert influence through multifactorial interactions and teleconnections. Internal circulation factors, including surface, subsurface, and deep soil moisture (SWVL1, SWVL2, SWVL3), regulate local moisture cycling. Nevertheless, SWVL1 together with vapor pressure deficit is shown to have a stronger and shorter-term impact, while impacts of SWVL2 and SWVL3 are weaker. These findings provide a robust framework for identifying key drivers of sub-seasonal EP and offer valuable insights for disaster prevention and mitigation strategies as we as for improving future hydro-climatic scenarios in arid and semi-arid regions.

How to cite: Chen, T., Lyu, H., Zhu, Y., Fu, Y., Magnini, A., and Castellarin, A.: Causality of Sub-seasonal Extreme Precipitation in Arid and Semi-Arid Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4310, https://doi.org/10.5194/egusphere-egu25-4310, 2025.

Rising temperatures are increasing the liquid fraction of precipitation in mountainous regions. This change, added to other changes in dynamic and thermodynamic processes generating heavy precipitation, could determine a potential intensification of the flood regime, posing increasing hazards to the population. In this study we aim at quantifying the projected change in liquid sub-daily precipitation extremes in the Greater Alpine Region. We use an ensemble of convection-permitting climate models (CPM) provided by the CORDEX-FPS Convection project at 1 hour temporal resolution and remapped to 3 km spatial resolution, covering historical (1996-2005) and far future (2090-2099) time periods under the RCP8.5 scenario. Total precipitation extremes are estimated from the total precipitation time series by identifying the independent storms, extracting the ordinary events and using the Simplified Metastatistical Extreme Value (SMEV) approach. Temperature is then used to separate the liquid and solid fraction of the identified storms, and the liquid and solid precipitation extreme quantiles are estimated. The results for the historical period are validated using station-based statistics of liquid precipitation in the Eastern Italian Alps. Comparing future changes obtained for total, liquid and solid precipitation, our study shows a strong elevation-dependent signal of liquid precipitation extremes amplification over the domain across the entire range of precipitation severity, which is predominant at daily durations. On the contrary, at hourly duration no statistically significant signal of liquid precipitation amplification could be extracted. Advancing earlier results by Dallan et al. (2024), this study highlights that the changes in liquid precipitation are enhanced more at daily duration, typically affected by dynamic factors and processes, than at hourly duration, for which thermodynamics plays a major role. Obtaining robust estimates of these changes is crucial for better managing water resources and designing adaptation strategies. This is particularly important for infrastructures such as dams, which are often located at high elevation and so are strongly impacted by changes in the liquid-solid phase separation.

How to cite: Pesce, M., Dallan, E., Marra, F., Fosser, G., Vohnicky, P., and Akbary, R.: Warming-induced increase in liquid fraction amplifies sub-daily rainfall extremes in the Greater Alpine Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9309, https://doi.org/10.5194/egusphere-egu25-9309, 2025.

Four of the largest river basins in Europe – Rhine, Rhône, Po, and Danube – are fed by Alpine water resources. Recent hydrological extremes, including catastrophic floods and prolonged droughts, have highlighted the vulnerability of these basins to climatic variability, with significant consequences for downstream populations, economies, and ecosystems. Understanding the potential drivers behind changes in streamflow patterns, particularly the relative contributions of precipitation and temperature, is essential for improving the attribution of extreme hydrological events and informing sustainable freshwater resource management. However, relatively short instrumental hydroclimatic records (i.e., precipitation, temperature and streamflow) in the European Alps limit our understanding of the long-term influence of climate variability on hydrological extremes. Here, by integrating paleo streamflow reconstructions, paleo climatic reanalysis, and climate model simulations, we examine how past and future variability in precipitation and temperature has influenced extreme hydrological events. Through advanced statistical and machine learning approaches, we quantify the relative contributions of precipitation and temperature to observed, reconstructed and projected streamflow anomalies, exploring their respective roles in triggering extreme flood and drought events. By comparing historical trends with future projections across different climate scenarios, we aim to identify the primary climatic drivers of hydrological extremes and their evolution over time. This work highlights the need for a better understanding of long-term climatic forcing mechanisms to improve attributions of hydrological extremes and develop robust adaptation strategies for the Alpine region and its vital river basins.

How to cite: Guo, R., Nguyen, H., Galelli, S., Ceola, S., and Montanari, A.: Long-Term Influence of Climate Variability on Hydrological Extremes across European Alpine Rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14526, https://doi.org/10.5194/egusphere-egu25-14526, 2025.

Efforts in enhancing resolutions of climate models are largely motivated by improving the representation of precipitation. Accurately capturing precipitation is key for understanding how a variety of natural hazards will change in the future, such as floods and droughts. Recent advances of climate models within HIGHRES-MIP to 50 km resolution and higher showed significant improvements in representing precipitation compared to CMIP6, particularly that associated with frontal systems of the mid-latitudes often manifesting as Atmospheric Rivers (ARs). Here, we leverage these new capabilities to study the sensitivity of high river streamflows on land to warming. We do so by utilizing the coupled atmosphere and land surface model AM4/LM4.0 developed at the Geophysical Fluid Dynamics Laboratory (GFDL). By applying a lagged correlation analysis between streamflows of the coupled river network and its upstream drivers, we identify major changes in drivers of high flows in response to a simple pseudo global warming experiment that yields a spatially homogeneous precipitation increase across most of the US.

We find that changes in high river flows show a strong dipole pattern across the US with increases in the East and decreases in most of the West. The increase in high-flows over the Eastern US is driven by an increase in precipitation-driven high-flows that exceeds the reduction in melt-driven high-flows. Among precipitation-driven high-flows, ARs contribute most to the increase. The reduction of high flows across the central and Western US is explained by significantly weaker snowmelt in spring. Here, increases in precipitation with warming, particularly from ARs, are counteracted by increased evaporation causing streamflows to dwindle. A Budyko-Analysis reveals that the disconnect of changes in precipitation and high flows can be explained by the energetic potential of the land-surface to evaporate the additional precipitation. While this potential is high over the central and Western US, it is low over the Eastern US. Finally, the overall reduction of snowmelt is found to alter the seasonality of high-flows with warming, for example in different sub-basins of the Mississippi where the month of peak high-flows shifts from March to May.

How to cite: Prange, M., Zhao, M., Shevliakova, E., Hong, M., and Malyshev, S.: How the response of high river flows to warming across the US is only loosely tied to precipitation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20427, https://doi.org/10.5194/egusphere-egu25-20427, 2025.

How to cite: Zhang, G. and Wu, Y.: Will drought evolution accelerate under future climate?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1819, https://doi.org/10.5194/egusphere-egu25-1819, 2025.

This study examines how climate change impacts hydrological patterns in a heavily contaminated Scottish catchment, focusing on extreme events like floods and droughts. By analysing historical trends, projecting future scenarios, and modelling contaminant transport, it highlights the challenges of predicting hydrological extremes and their implications for water quality and environmental management.

Adapting hydrological models to account for future climate conditions is complex, particularly when predicting extreme events like floods and droughts. Traditional models, calibrated using historical data, often fail to capture hydrological behaviour in a rapidly changing environment. This research addresses these challenges by testing calibration techniques to enhance model performance across various flow conditions and contaminant transport processes.

A key difficulty lies in balancing model sensitivity to both high-flow events, which drive rapid contaminant transport, and low-flow conditions, where contaminants persist due to slower water movement. Techniques such as parameter sensitivity analysis and statistical optimization methods—like Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE)—are employed to ensure models accurately represent diverse hydrological conditions. These models are validated under different climate scenarios to predict future extreme events and contaminant behaviours.

Multi-objective calibration techniques, which account for high- and low-flow dynamics, prove more effective in predicting future hydrological extremes. Metrics like peak flow rates, baseflow, NSE, and RMSE assess performance, aiding in flood mitigation and water quality risk management. By improving model robustness, this approach provides critical insights into water and contaminant movement under varying flow scenarios, supporting better preparedness for climate-driven challenges.

The River Almond catchment (375 km²) is one of Scotland’s most polluted river systems, shaped by industrial shifts, agricultural intensification, and urbanisation. These activities have created pollution hotspots, with pharmaceuticals, pesticides, nutrients, and endocrine disruptors as key contaminants. Their transport is closely tied to water flow dynamics, with hydrological signatures offering critical insights, especially during extreme events.

Periods of extreme rainfall or drought significantly influence contaminant behaviour. High-flow events mobilize contaminants like ibuprofen, while endocrine disruptors such as bisphenol A display flow-dependent patterns influenced by location and intensity. Rainfall after prolonged dry periods drives sudden spikes in the movement of pollutants, particularly microplastics, emphasizing the role of rain pulses in dispersion.

This study uses hydrograph analysis to assess contaminant responses to water flow fluctuations. Floods are expected to accelerate long-distance pollutant transport, while droughts may concentrate contaminants in stagnant water or sediments, creating latent risks reactivated by subsequent rainfall.

As climate change intensifies flow variability, understanding these pathways is essential for improving water quality management, mitigating pollution risks, and safeguarding the River Almond catchment’s resources.

How to cite: Corrochano- Fraile, A. and Beevers, L.: Hydrological modelling: Insights into hydrological signals and contaminant transport, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6009, https://doi.org/10.5194/egusphere-egu25-6009, 2025.

This study explores the stochastic analysis of wind and solar meteorological processes, focusing on simultaneous simulations at small scales while preserving marginal distribution, periodicities and dependence structure. Using historical data from Amsterdam Schiphol Airport as a case study, the analysis employs the Hurst-Kolmogorov process to model variability and long-term dependence.

By integrating the Hurst-Kolmogorov framework with recent stochastic modelling algorithms, synthetic time series are generated to emulate realistic patterns of wind and solar variability. Special attention is given to assessing the correlation between wind and solar processes, as their interplay significantly influences the balance and reliability of renewable energy systems.  These simulations aim to enhance the reliability of renewable energy resource assessments, supporting decision-making for infrastructure design and offering practical applications beyond the case study to broader renewable energy systems planning.

How to cite: Balachtari, D., Iliopoulou, T., Dimitriadis, P., Mamassis, N., and Koutsoyiannis, D.: Stochastic simulations of wind and solar processes for reliable renewable energy decision-making, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6897, https://doi.org/10.5194/egusphere-egu25-6897, 2025.