PICO
This session particularly welcomes, but is not limited to, contributions on:
- Advanced techniques to simulate and predict hydrological processes and water resources, with emphasis on stochastic and hybrid methods.
- Advanced techniques to simulate and predict hydroclimatic extreme events including compound extreme events (e.g. heatwaves, floods and droughts).
- Holistic approaches to generate future water resources scenarios integrating also anthropogenic and environmental perspectives.
- Hydroclimatic change attribution studies using probabilistic approaches and novel causality frameworks with uncertainty assessment.
- Evaluation of climate models performance at regional and local scales using observational data
This session is supported by the International Association of Hydrological Sciences (IAHS), the World Meteorological Organization, the National Recovery Resilience Plan RETURN Foundation of Italy, and it is also related to the scientific decade 2023–2032 of IAHS, “HELPING”.
Four of the largest river basins in Europe – Rhine, Rhône, Po, and Danube – rely heavily on Alpine headwaters. Recently, these basins have experienced intensifying hydroclimatic fluctuations, including catastrophic floods and prolonged droughts, highlighting the vulnerability of these basins to climatic variability. 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 in the European Alps limit our understanding of the long-term influence of climate variability on hydrological extremes. This research integrates proxy-based reconstructions with paleo-climate reanalysis to assess streamflow variations over an extended timeframe. Through statistical regression, we quantify how changing rainfall and temperature patterns contribute to the onset of extreme events, with a specific focus on recent droughts. 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 emphasizes the necessity of long-term perspectives in attributing extreme events and securing water resources in the Alps.
How to cite: Guo, R., Nguyen, H., Galelli, S., Ceola, S., and Montanari, A.: Increasing Influence of Temperature on Recent Hydrological Extremes across European Alpine Rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1304, https://doi.org/10.5194/egusphere-egu26-1304, 2026.
Snow plays a crucial role in Europe’s hydrological cycle, influencing water availability,
river runoff, and seasonal storage in mountain and northern regions that support both
national and transboundary water systems. This study examines key snow-related
processes including snowfall, snow cover fraction, and snow water equivalent across
Europe. Stochastic approaches are applied to quantify long-term persistence
characteristics in snow and hydrological processes. Synthetic scenarios are further
generated to assess potential responses of snow under regional warming. The
results are expected to provide insights into European snow dynamics and their role
in shaping seasonal streamflow and water availability. This study offers a Europe-
wide stochastic perspective on cryosphere–hydrosphere interactions under climate
variability, where changes in snow storage and melt dynamics can affect hydropower
production, agricultural water demand, and drinking water supply.
How to cite: Bozanas, I. F., Sargentis, G.-F., and Iliopoulou, T.: European snow dynamics and changing patterns under climate variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10737, https://doi.org/10.5194/egusphere-egu26-10737, 2026.
Recurring droughts under climate variability pose increasing challenges for drinking-water supply and irrigation. This study compares the temporal dynamics of aridity in selected European and African regions. Although these areas lie within similar latitude bands according to the IPCC SREX classification, they differ substantially in climate regimes and adaptive capacity. Aridity dynamics are assessed using key hydroclimatic variables—precipitation, temperature, evaporation and potential evapotranspiration—together with established aridity indices. Temporal changes are analyzed, and the stochastic structure of climatic variability is evaluated within the Hurst–Kolmogorov framework. The results are interpreted in relation to regional water infrastructure and its role in shaping the capacity to cope with evolving aridity conditions.
How to cite: Maravitsas, K., Makris, P., Bousoula, T., Sargentis, G.-F., and Iliopoulou, T.: Comparison of temporal changes in aridity in European and African regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9974, https://doi.org/10.5194/egusphere-egu26-9974, 2026.
This project investigates the hydroclimatic variability of Cyprus through an integrated statistical and spatial analysis framework, with emphasis on precipitation and streamflow extremes. The main objectives are to (i) characterize the spatial and temporal variability of rainfall and runoff across the free territory of the Republic of Cyprus by identifying patterns, trends, and extreme events, and (ii) evaluate the stability of rainfall–runoff relationships under both natural and regulated hydrological conditions.
The analysis uses long-term, quality-controlled precipitation and streamflow datasets from Cyprus, comprising 167 daily rainfall series (up to 107 years) and 45 hydrometric records (up to 58 years). Analyses were conducted for multiple minimum record lengths to assess record-length effects and were supported by documented drought events identified using the Standardized Precipitation Index. Spatial rainfall patterns were examined using Inverse Distance Weighting and Ordinary Kriging, while rainfall–runoff relationships were quantified for 12 station pairs using correlation analysis of mean and maximum annual values.
The results demonstrate that mean annual rainfall remains largely stable across all examined temporal scales, indicating long-term stability of average hydroclimatic conditions, whereas maximum annual rainfall exhibits a slight increasing tendency across all records, suggesting a gradual intensification of extreme events rather than changes in total rainfall amounts. Record statistics show consistency between low rainfall records and documented drought periods. Spatial analyses highlight the dominant orographic influence of the Troodos mountain range, with rainfall amounts and variability increasing with elevation, while rainfall–runoff correlations weaken in catchments regulated by hydraulic structures.
Overall, the results indicate a persistent mean hydroclimatic regime accompanied by gradual intensification of extreme precipitation events without a corresponding change in total annual rainfall. The island’s orography remains the dominant control on rainfall patterns, while increasing anthropogenic intervention disrupts the hydrological response.
How to cite: Georgiou, T., Iliopoulou, T., and Koutsoyiannis, D.: Hydroclimatic Variability of Cyprus with Emphasis on Extreme Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18542, https://doi.org/10.5194/egusphere-egu26-18542, 2026.
The Mediterranean region is often described as a “climate change hotspot” in model projections due to pronounced warming signals. However, recent empirical analyses indicate that the hydrological response to regional warming is more nuanced and complex than represented by climate models. In this study, we examine the co-variability of several atmospheric and land–surface drivers that influence the behaviour of key hydroclimatic variables—precipitation, temperature, and evaporation—across the Mediterranean domain. The analysis is based on the ERA5 reanalysis dataset and explicitly distinguishes between land and sea domains to account for their differing dynamical and thermodynamic characteristics. To assess the strength and structure of associations, we employ complementary approaches including feature-importance metrics from machine-learning models and a revised formulation of the impulse response function based on the stochastic covariance structure, suitable for hydroclimatic dynamics. We investigate how different drivers relate to each other across space and scales, and we discuss methodological implications for developing more reliable hydroclimatic scenarios for water-resources and climate-impact studies.
How to cite: Iliopoulou, T., Tepetidis, N., and Koutsoyiannis, D.: Co-variability and relative strength of hydroclimatic drivers in the Mediterranean region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22780, https://doi.org/10.5194/egusphere-egu26-22780, 2026.
Identifying the drivers of a process is imperative to its understanding and forecasting, especially under changing climate. Hydrometeorological systems are complex with multiple closely related variables. In such systems a process can have multiple drivers, coupled to the system, across timescales. Thus, identifying the drivers of a process becomes challenging. In Hydrology, multivariate regression and recently Big Data machine learning methods have gained popularity. However, these methods rely on finding correlation between variables and fall short of identifying causal (cause-effect) relations.
This work explores causal discovery (CD) algorithms to identify the drivers in a hydrological system. Specifically, we evaluate the following four theoretically distinct multivariate CD algorithms, (i) TCDF (ii) VARLiNGAM, (iii) PCMCI+, and (iv) DYNOTEARS. We evaluate these algorithms within a large and complex simulated environment of the Global Land Data Assimilation System (GLDAS) where the drivers, reference truth, are known perfectly. We evaluate the drivers identified by CD methods against this reference truth and contrast its results with the widely used method of co-relation identification, Pearson’s Correlation Coefficient (PCC). While identifying a causal link is important to understand cause-effect relations between variables, eliminating spurious correlation as false causality is also important to obtain a parsimonious set of predictors. Accordingly, we evaluate the performance of CD methods and PCC for both these aspects.
The results show that CD methods identify fewer false drivers compared to PCC, which is prone to spurious associations from cross-correlations and lagged correlations, typically present in hydrometeorological systems. In contrast, CD methods eliminate a higher number of false instantaneous and lagged drivers. Thus, although PCC identifies the highest number of true drivers, it suffers from a high number of false drivers. Overall, CD methods perform similar to or better than PCC, with PCMCI+ and DYNOTEARS performing the best.
Further, we evaluate the effect of focusing on causal drivers by training machine learning models for surface soil moisture prediction. We evaluate their performance under changing climate conditions of drought. PCC-based models show higher performance in the training period (median R2=0.85 & NSE=0.84); however, they suffer a sharp drop in performance during the test period. In contrast CD-based models show decent performance in training (median R2~0.8 & NSE~0.78) and are more robust in the testing period. Together, these findings highlight the value of CD for eliminating spurious relations and retrieving a robust, parsimonious set of predictors for process understanding and predictions under diverse climate conditions.
This study overviews, demonstrates and tests the efficacy of CD methods in identifying cause-effect relations in hydrometeorological systems. By exposing their capabilities and differences in a simulated environment, we hope to encourage their use in the real world and move beyond co-relation.
How to cite: Yadav, V. K., Peel, M., Fowler, K., Ryu, D., and Vishwakarma, B. D.: Cause-effect based modelling for reliable results under changing climatic conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9920, https://doi.org/10.5194/egusphere-egu26-9920, 2026.
Droughts are among the most intense and impactful natural hazard-related disasters facing humanity. The European Commission’s adaptation strategy report (2021) highlights that water scarcity increasingly disrupts a wide spectrum of socioeconomic aspects, ranging from agriculture and food industry to enhancing social and gender inequalities and consequently leading to human, material and economic losses. Therefore, efforts for creating resilient societies, able to deal with climate induced hazards, are high on the agenda both in European and global level. For achieving this universal objective, one of the primary steps suggests deeper understanding of the natural processes responsible for the hazards. In the case of droughts, the detailed study of precipitation patterns and the use of appropriate stochastic simulation methods, which capture the inherent characteristics of natural processes, are crucial for drought risk estimation and forecasting. In this work, we use the state-of-the-art stochastic modeling framework CoSMoS, which implicitly, in terms of the autocorrelation function, and explicitly, in terms of the probability distribution function, adequately simulates the expected variability and interdependence characterizing precipitation records. The stochastic scheme is applied to the precipitation time series of Bologna, which; being one of the longest rainfall time series worldwide, provides a significant advantage in the field of stochastic generation. Following a Monte Carlo simulation approach, 500 synthetic precipitation time series of 100 years each are generated and subsequently analyzed applying run theory to estimate drought risk, frequency and duration, across the city of Bologna and the adjacent provinces. Rather than relying on urgent adaptation measures during a water crisis, the findings and generally the application of the methodology followed in this study, foster a proactive approach in drought management and offer valuable insights in urban and water resources planning, public awareness initiatives, insurance risk assessment and encourage legislative amendments. By integrating probabilistic and statistical methods in drought risk analysis, this work contributes to the global demand to strengthen the resilience of societies against climate related risks.
How to cite: Vrettou, S., Koutsoyiannis, D., Dimitriadis, P., Iliopoulou, T., and Montanari, A.: Integration of stochastic and statistical approaches for drought risk estimation through long-length timeseries, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1107, https://doi.org/10.5194/egusphere-egu26-1107, 2026.
Accurate precipitation estimations are crucial for hydrological modelling and water resource management, especially in geographically complex regions like Greece. Satellite-based products are valuable as they encompass extensive spatial coverage with high data density, but their accuracy is limited compared to ground truth measurements. To address this bias, we leverage machine learning (ML) approaches. We present a hybrid machine learning framework that employs post-processing techniques to integrate satellite-derived precipitation data with ground-based gauge observations. The methodological framework upgrades a deterministic ML regressor (D-model) into a fully stochastic system (S-model) using Bluecat methodology. We use data for the period 2000-2021 over Greek territory, from gauge observations and Integrated Multi-Satellite Retrievals for GPM (IMERG). The S-Model significantly improves reliability and statistical consistency, effectively transforming the ML output into actionable, risk-aware intelligence.
How to cite: Tepetidis, N., Iliopoulou, T., Dimitriadis, P., Benekos, I., Montanari, A., and Koutsoyiannis, D.: From Deterministic to Stochastic: A Hybrid Machine Learning Framework for Reliable Satellite Precipitation Merging over Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5936, https://doi.org/10.5194/egusphere-egu26-5936, 2026.
The Indian coastline, flanked by the Bay of Bengal and the Arabian Sea, is prone to the impact of intense low-pressure systems, specifically tropical depressions and storms, which are accompanied by extreme rainfall and storm surges. The vulnerability of the Indian East Coast to compound flooding, characterized by the concurrent occurrence of extreme rainfall and wind-driven extreme storm surges, poses a significant challenge in the face of changing climatic conditions. This study examines how anthropogenic climate change may influence the frequency and intensity of such compound extremes on the Indian East Coast by the end of the 21st century. For this purpose, observed sea level data at three tide gauge (TG) stations (Paradip, Haldia, and Chennai) were used to extract storm surge time series for the period 1980-2010. Daily rainfall was obtained from the 0.25° gridded dataset of the India Meteorological Department (IMD), while mean sea level pressure anomalies and surface wind speeds were extracted from ERA5 reanalysis data within a 500 km radius of the coastal stations along the East Coast of India. A logistic regression model was utilized to identify the suitable atmospheric predictor for storm surge extremes, and the corresponding threshold of the variable leading to storm surge extremes (exceeding the 95th percentile) at the tide gauge station was identified. Subsequently, the bias-corrected GCM simulated precipitation and wind stress (identified from a logistic regression model) variables were obtained at the grid points near the TG stations from 10 models corresponding to CMIP6 simulations for the historical period as well for the end of the century (2070-2100), corresponding to the extreme ssp585 scenario. The compound extremes were identified in the GCM data for both the historical and future periods by using thresholds of simulated rainfall and wind stress (identified from logistic regression) data consistent with those derived from observations. The change in seasonal, annual and decadal variability of the frequency of the compound extremes was investigated for data from each of the 10 models as well the ensemble mean from the models for the future period with respect to the historical period. Initial results show a greater change in the frequency of these extremes in the post-monsoon season than the monsoon season for the majority of the models. Additionally, a higher mean annual intensity of the compound extremes with respect to the historical counterpart was expected to occur under the SSP585 scenario at the end of the century. The synoptic patterns corresponding to the compound extremes were also investigated to understand the changing dynamics of these extremes on the Indian Coast.
How to cite: Dutta, D., Venkata Vemavarupu, S., and Bala, G.: Assessing the Impact of Climate Change on Frequency and Intensity of Compound Coastal Extremes in India , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-439, https://doi.org/10.5194/egusphere-egu26-439, 2026.
The frequency of extraordinary floods (EF) has risen globally in recent years, often accompanied by substantial economic losses and fatalities. However, these changes are not uniform and exhibit pronounced spatial and temporal variability. Extreme precipitation (EP) is considered one of the key factors triggering EF and is associated with various weather systems. However, studies examining how different types of EP contribute to EF, particularly in terms of the spatiotemporal variability of these relationships in response to climate change, remain limited.
This study proposes a framework to assess the impacts of different types of EP on the occurrence of EF. We use data from Taiwan as a case study, where multiple flood-associated types of EP occur, including tropical cyclones (TCs), mesoscale convective systems (MCSs), and frontal systems (FSs). To examine differences among EP-EF groups, a mechanism-based framework is developed to classify EP types and flood events. Meanwhile, weather types are identified using an unsupervised k-means clustering approach based on three groups of variables: precipitation, storm-related characteristics, and topographic controls. EF events are defined using a peak-over-threshold (POT) approach and are linked to their corresponding weather types. Our findings reveal varying temporal trends across different EP-EF groups, providing insights into how the spatiotemporal variability of extreme rainfall affects the occurrence of extraordinary floods.
How to cite: Wu, H. and Wang, H.-J.: Disentangling the Mechanisms Linking Extreme Precipitation Types to Extraordinary Floods: An Assessment in Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6159, https://doi.org/10.5194/egusphere-egu26-6159, 2026.
As climate change intensifies the variability of precipitation patterns, understanding the shifts in Intensity-Duration-Frequency (IDF) characteristics is becoming essential for sustainable water resource management. This study aims to investigate the long-term trends of extreme rainfall events across the major river basins of South Korea and evaluate their potential implications for regional flood risk.
The research framework focuses on identifying the transition of rainfall intensities by comparing historical observations with future climate projections. By analyzing how the relationship between rainfall duration and frequency evolves, we expect to characterize the changing nature of hydrologic extremes in different geographical contexts. Furthermore, this study explores the link between these shifting IDF curves and their impact on basin-scale flood responses, aiming to provide a comprehensive assessment of infrastructure resilience.
The anticipated findings will offer a fundamental basis for understanding hydro-climatic risks and contribute to developing more adaptive flood mitigation strategies. This work serves as a preliminary step toward bridging the uncertainty in climate data and practical engineering applications for future-ready disaster management.
How to cite: Pak, H., Kwak, G., and Yang, J.: A Study on the Response of Flood Vulnerability to Changes in IDF Characteristics across Major River Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16191, https://doi.org/10.5194/egusphere-egu26-16191, 2026.
Assessing the impact of climate change on water availability requires robust and spatially consistent modelling frameworks, particularly in Mediterranean regions characterized by strong hydroclimatic variability. The Mediterranean basin is widely recognized as a climate-change hotspot, where increasing temperatures and changes in precipitation regimes are expected to exacerbate water scarcity and hydrological extremes, especially in southern areas (Lazoglou et al., 2024). This study investigates future water availability across Southern Italy, with a specific focus on the Southern Apennines River Basin District, by coupling high-resolution climate projections with a large-scale gridded hydrological model.
Future climate forcing was derived from the Global Climate Model MPI-ESM-1-2-HR under the SSP5-8.5 scenario and dynamically downscaled at a convection-permitting scale (~4 km) using the WRF model. Bias-corrected precipitation and air temperature fields were subsequently re-gridded to 1-km resolution and used to force HYGRID-M (an acronym for HYdrological GRIDed – Monthly), a distributed water balance model operating at monthly time scale. HYGRID-M simulates actual evapotranspiration and runoff by integrating climatic inputs with spatially distributed information on land use, soil properties, and topography. The modelling framework was applied to simulate water balance components for the future period 2025–2044 and compared with a observed baseline (2000–2023), analyzing changes in precipitation seasonality and temperature, together with their impacts on evapotranspiration and runoff at both temporal and spatial scales.
Results indicate a marked reduction in summer precipitation and a consistent increase in air temperature across all months, with warming reaching approximately +2°C. Despite higher temperatures, both actual evapotranspiration and runoff exhibited predominantly negative anomalies relative to the observed period, reflecting increased water limitations rather than a persistent long-term decreasing trend. Actual evapotranspiration (AET) exhibited yearly variations ranging from -29% to +16% with a mean of -2% and a standard deviation of 10.2% while runoff (Q) ranged from –39% to +46%, with a mean of –1.3% and a standard deviation of 24%, indicating the strong interannual variability with alternating dry and wetter years. The negligible contribution of snow accumulation and melting under future climatic conditions further alters seasonal runoff dynamics. Spatially, evapotranspiration responses were heterogeneous, with localized increases in Puglia and parts of Basilicata, whereas runoff showed mixed signals, with widespread reductions across Campania, Molise, Abruzzo and parts of Calabria.
Overall, the results highlight a shift toward drier and more variable hydroclimatic conditions in Southern Italy, emphasizing the importance of integrated high-resolution climate–hydrological modelling for supporting climate adaptation and sustainable water resource planning at river basin scale.
How to cite: Dal Sasso, S. F., Aung, H. H., Furnari, L., Senatore, A., and Mendicino, G.: Water availability assessment across Southern Italy under future climate scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14786, https://doi.org/10.5194/egusphere-egu26-14786, 2026.
Periods of low Renewable Energy Sources (RES) production are critical for power system reliability. This study investigates the stochastic dynamics linking solar radiation, wind conditions, and renewable energy production in Greece. Emphasis is placed on characterizing energy droughts by analyzing temporal variability and persistence features of the relevant climatic variables using the Hurst–Kolmogorov framework. Based on historical datasets, we identify prolonged deficit periods and quantify the probability of critical concurrent low-wind and low-solar events. Finally, synthetic future scenarios are generated to estimate energy storage requirements, in order to support resilient infrastructure design under the inherent stochastic variability of climatic processes.
How to cite: Pagoulatou, P., Mandilaki, E., Iliopoulou, T., Sargentis, G.-F., and Ioannidis, R.: Stochastic Investigation of Solar and Wind Processes for Renewable Energy Storage in Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9942, https://doi.org/10.5194/egusphere-egu26-9942, 2026.
Social prosperity fundamentally relies on the sustainable management of natural resources. In the contemporary world, however, this
perspective has been distorted, as economic optimization increasingly dominates resource allocation decisions, often prioritizing short-term financial gains over long-term societal and environmental benefits. To highlight this distortion, we evaluate a planned pumped-storage hydropower (PSH) project in Northern Euboea, Greece, using two contrasting operational frameworks:
- Resource- and needs-oriented approach (socio-environmental perspective): The PSH system is coupled with renewable energy sources (RES) and operated to optimize water and energy resource use, ensuring stable and reliable energy supply aligned with actual societal demand, irrespective of short-term market fluctuations.
- Market-driven approach (economic optimization perspective): The system exploits price volatility in the energy exchange market by pumping (storing energy) when electricity prices are low and generating (turbine operation) when prices are high, aiming to maximize economic profitability.
We analyze the stochastic properties and dynamics of relevant time series — including RES production, electricity market prices, and
demand patterns — to quantify and compare system behavior under each paradigm. Key metrics include resource efficiency, supply
reliability, economic returns, and alignment with broader sustainability goals. The results reveal fundamental tensions between the two approaches: the market-driven strategy yields higher short-term revenues. In contrast, the needs-oriented operation better supports long-term social prosperity and resource conservation, though at the potential cost of lower immediate financial performance. This comparative analysis underscores how the dominance of market mechanisms can distort natural resource management and advocates for a reorientation of decision-making criteria toward long-term societal well-being and environmental
sustainability in energy infrastructure planning.
How to cite: Saperopoulou, D., Kouzelis, V., Sargentis, G.-F., Efstratiadis, A., and Tepetidis, N.: Social prosperity and natural resource management: Stochastic evaluation of two operational paradigms of pumped-storage hydropower in North Euboea under renewable energy integration and energy market dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8099, https://doi.org/10.5194/egusphere-egu26-8099, 2026.
