We welcome abstracts capturing recent insights for understanding past, present, and future impacts of internal/natural climate variability on hydrological systems and extremes, as well as newly developed probabilistic and storyline impact scenarios. Results from model intercomparisons using large ensembles are encouraged.
Orals: Thu, 7 May, 14:00–15:45 | Room 2.15
Whenever record-breaking flood and drought events occur, they are held up as a manifestation of anthropogenic warming – which is entirely reasonable given physical reasoning and typical projections for the future. However, to contextualise such claims it is also vital to analyse long-term observations of river flow to detect and attribute emerging trends. While there is often good agreement between these lines of evidence, there are sometimes discrepancies in the strength or even the direction of change in observations compared to climate projections. This can present a profound challenge to policymakers and adaptation planners: how to proceed given deep uncertainty in future projections, especially if conflicting with lived historical experience?
In this presentation, we tackle this question using hydrological droughts in the UK as a case study. Recent major droughts[LB1] (including in 2025) have led to growing concerns that droughts are becoming more severe in the UK, despite it generally being perceived as a wet country. Firstly, we appraise the evidence for any trends towards worsening hydrological droughts in the UK. The UK has a well-established monitoring programme and hence provides a good international case study for addressing this question. We assess the evidence for changes in the well-gauged post-1960 period, before considering centennial scale changes using reconstructions. A further challenge with hydrological extremes (compared to climate variables) is that observed trends in river flows can reflect catchment alterations rather than climatic variability. Hence, we provide a synthesis of our understanding of the drivers of change in hydrological drought, both climatic and in terms of direct human disturbances to river catchments (e.g. changing patterns of water withdrawals, impoundments, land use changes). These latter impacts confound the identification of climate-driven changes, and yet human influences are themselves increasingly recognised as potential agents of changing drought regimes. Perhaps surprisingly, we find little evidence of compelling changes towards worsening drought, apparently at odds with climate projections for the relatively near future and widely-held assumptions of the role of human disturbances in intensifying droughts. Nevertheless it leaves water managers and policymakers at an impasse.
Hence, we set out recommendations for guiding research and policy alike. Two major themes emerge: 1) integration of observational trend studies with hydroclimate modelling using ‘large ensemble’ approaches, seeing the observed past as only one instance among ‘worlds that might have been’ to help better frame emerging risks and develop stress tests; 2) improved understanding of the drivers of change, moving beyond largely correlation-based links with climate forcings towards understanding underlying atmosphere-oceanic processes, while simultaneously better discriminating the ‘human factor’ (i.e. water withdrawals or land use) – a grand challenge but one which new datasets and methods are making more feasible. While our focus is the UK, we envisage the themes within this presentation will resonate with the international community and we conclude with ways our findings are relevant more broadly.
How to cite: Hannaford, J., Turner, S., Chevuturi, A., Chan, W., Barker, L., Tanguy, M., Parry, S., Allen, S., and Facer-Childs, K.: Drought variability in a wet country (the UK): when observation-based trends and hydroclimate projections disagree, how might we move forward? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16270, https://doi.org/10.5194/egusphere-egu26-16270, 2026.
Understanding historical and future changes to seasonal and extreme flow regimes is crucial for both water resources planning and flood risk management. Historically, inter-annual to multi-decadal variability in seasonal flow has been influenced by variability in atmospheric circulation over the UK, and by long-term changes in the mean state of atmospheric circulation. Having trust in future hydrological projections therefore requires (1) a thorough understanding of the representation of this atmospheric circulation induced variability in climate models, and (2) confidence that climate models are capable of reproducing periods of atmospheric circulation patterns associated with wet or dry conditions.
In this work, we apply a novel dynamical adjustment methodology based on synoptic-scale weather patterns to a century long reconstruction of seasonal standardised streamflow index (SSI) for catchments in the UK. This methodology isolates the influence of atmospheric circulation variability on SSI, exhibiting clear seasonality and spatial patterns. In some catchments, this ‘dynamical’ SSI component explains a high proportion of variability in total seasonal SSI.
Using the same synoptic-scale weather patterns, we find that UKCP18 climate models underestimate seasonal variability in the dynamical component of SSI. We demonstrate that the differences in distribution between observations and model simulations must be due to differences in weather pattern frequency and/or clustering, rather than rainfall biases. Our findings raise questions about the suitability of climate models in projecting streamflow trends and understanding future seasonal extremes.
How to cite: Murgatroyd, A., Carruthers, J., and Fowler, H.: Dynamically induced streamflow variability in UK river catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10692, https://doi.org/10.5194/egusphere-egu26-10692, 2026.
Summer droughts have become more frequent and severe in Central Europe, threatening water security and ecosystem resilience. In this study, we examine the link between large-scale climate variability, particularly the winter North Atlantic Oscillation (NAO), and summer hydroclimate and drought propagation across the region. We combine teleconnection diagnostics, reanalysis data, and a process-based, isotope-enabled ecohydrological model to assess how winter NAO variability influences summer droughts and their propagation through the Soil–Plant–Atmosphere Continuum (SPAC) within a representative lowland catchment in the North European Plain. Positive NAO phases in winter are associated with reduced summer precipitation and sustained deficits in soil moisture, streamflow and groundwater, indicating hydrological responses with a lag of up to ten months. We also found that winter precipitation has become less sensitive to NAO variability, while summer droughts are now more strongly linked to preceding positive winter NAO phases, likely reflecting climate-driven changes in atmospheric circulation. Integrating large-scale atmospheric variability with local ecohydrological processes sheds new light on how internal climate modes modulate drought propagation and provides new opportunities to improve seasonal drought prediction and adaptive water-resource planning in Europe’s drought-sensitive landscapes.
How to cite: Jiang, C., Soulsby, C., Laudon, H., Wu, S., and Tetzlaff, D.: Projecting Summer Hydroclimate Extremes in Central Europe from Winter NAO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-274, https://doi.org/10.5194/egusphere-egu26-274, 2026.
Assessing future changes in hydrological extremes requires accounting for both externally forced climate change and internal climate variability, which can substantially modulate flood magnitude and frequency. Here, we synthesize observational and modelling evidence to examine how these drivers jointly shape flood regimes across South America, with implications for both flood risk and ecosystem dynamics.
Using multi-decadal streamflow observations, we show that internal climate variability associated with the El Niño–Southern Oscillation (ENSO) strongly alters the likelihood of extreme hydrological events. Flood probabilities increase by more than 120% during El Niño in the La Plata Basin and during La Niña in the northern Amazon. Streamflow extremes respond more strongly than precipitation, indicating cumulative hydrological amplification of climate variability.
Complementing these findings, hydrodynamic–hydrological simulations forced by the CMIP6 ensemble reveal heterogeneous future flood responses under climate change. Flood magnitude and frequency are projected to intensify markedly in southern Brazil, where events may become up to five times more frequent, while major wetlands such as the Amazon and Pantanal are projected to experience reduced flood occurrence, with potential negative ecological consequences. These contrasting responses arise from competing influences of increasing extreme precipitation and enhanced evapotranspiration, as well as substantial spread across climate model realizations.
Together, these results demonstrate that internal climate variability can amplify, mask, or temporally offset forced changes in flood regimes, leading to divergent but physically plausible outcomes.
How to cite: Petry, I. and Fan, F.: Observed Variability and Projected Change in South American Flood Regimes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11240, https://doi.org/10.5194/egusphere-egu26-11240, 2026.
Rainfall variability plays a key role in how we are impacted by flood events, but also how we manage our water storages for flood mitigation and replenish our water resources. The sequencing between wetter and drier periods typically results in a natural fluctuation of rainfall-streamflow response but also modulates streamflow extremes. This relationship is being be impacted by anthropogenic climate change, with unprecedented extreme events and changes to long-term catchment dynamics. Several factors contribute to how rainfall variability influences streamflow event variability, including the rainfall event total, rainfall frequency, rainfall intensity and antecedent catchment conditions. In this study, we investigate changes to these rainfall variability factors and their relationship to streamflow event variability.
Our investigation is performed at the catchment scale for 467 Hydrological Reference Stations (HRS) catchments across Australia, utilising catchment-aggregated daily rainfall and gauged daily streamflow from 1950 to 2022. We investigate long-term trends in the frequency, duration and intensity of wet and dry rainfall spells across annual and seasonal timescales. We also identify streamflow events for each catchment and calculate key hydroclimate conditions before and during the event, such as the length of the rainfall dry spell before the event. These conditions are then used to better understand the different drivers of streamflow event volumes across Australia.
We find that southern and eastern Australia experience a drying trend with more dry days, shorter wet spells and greater intermittency with increases in the number of wet and dry spells per year. Northern and northwestern Australia experiences a wetting trend with more wet days, longer wet spells and increases in annual rainfall totals and rain intensity. These results are seasonally dependent, with stronger trends during periods where the majority of rainfall falls. The most important factors in driving streamflow event volumes are rainfall and soil moisture. We also find that the relationship between dry spells and streamflow event volumes is weak across Australian catchments despite a strong correlation with annual streamflow volumes. This highlights that event scale dynamics differ from the annual scale and the need to expand this analysis of the drivers of streamflow events alongside drivers of annual streamflow.
How to cite: Thomas, S., Wasko, C., Guo, D., Bende-Michl, U., and Peel, M.: Understanding the interplay between rainfall intermittency and streamflow events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8647, https://doi.org/10.5194/egusphere-egu26-8647, 2026.
Understanding how groundwater systems will respond to climate change is essential for water-scarce regions such as the Algarve, southern Portugal, where groundwater plays a central role in sustaining agriculture and ecosystems. Previous studies in Portugal have demonstrated that climate teleconnections influence aquifer recharge processes across interannual to decadal timescales, with NAO identified as the dominant driver in southern Portugal and EA and SCAND contributing to higher-frequency variability. However, most existing analyses have focused on historical observations, offering limited insight into future groundwater behavior under projected climate change.
This study integrates climate mode analysis with deep learning-based projections to assess future groundwater variability in the Algarve. Spectral analyses of historical piezometric and precipitation records were first conducted to characterize dominant variability regimes and classify aquifers into annual, mixed, and low-frequency dominated systems. These classifications were then incorporated into deep learning models trained using CMIP6 climate model outputs, namely precipitation and temperature. Groundwater levels were projected under multiple Shared Socioeconomic Pathway (SSP) scenarios for mid-century (2030–2050) and late-century (2050–2100) periods.
The preliminary results indicate a general decline in groundwater levels across Algarve aquifers under all future climate scenarios, with the magnitude and temporal structure of change varying by aquifer type. Aquifers characterized by strong low-frequency variability exhibited more pronounced long-term declines, suggesting increased vulnerability to persistent climate forcing. In contrast, systems dominated by annual variability showed greater short-term responsiveness but less pronounced long-term trends. Across scenarios, a reduction in low-frequency variability was observed, indicating a potential loss of groundwater system inertia and reduced buffering capacity against prolonged droughts.
The analysis further suggests that climate teleconnections will continue to play a significant role in shaping projected groundwater dynamics, with NAO remaining the primary large-scale driver and EA and SCAND influencing higher-frequency modulations. The findings offer valuable guidance for regional groundwater management and provide a transferable framework for assessing climate-driven groundwater variability in other Mediterranean and Atlantic coastal regions.
This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 - https://doi.org/10.54499/LA/P/0068/2020 , UID/50019/2025, https://doi.org/10.54499/UID/PRR/50019/2025, UID/PRR2/50019/2025
How to cite: Tjugaeva, A. and Neves, M. C.: Projecting Climate-Driven Groundwater Variability in the Algarve using Deep Learning-Based Projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11823, https://doi.org/10.5194/egusphere-egu26-11823, 2026.
The growing global population and associated socio-economic development are increasing water demand. At the same time, the overexploitation of water resources, particularly in regions with limited availability, leads to mounting water scarcity that is expected to further intensify under projected climate and socio-economic change. Consequently, assessments of current and future water resources need to account for the coupled effects of climate change, human water management practices, and hydrological processes. Despite the widespread relevance of these interactions, significant gaps remain in our understanding of the interplay between (i) human water management, (ii) local-to-basin-scale hydrology, and (iii) hydroclimatological and atmospheric responses. A major reason for this is that many state-of-the-art Earth system models misrepresent or omit critical processes, such as river routing, sectoral water withdrawals, groundwater pumping, and dam/reservoir operations. These limitations constrain our ability to consistently quantify impacts across scales and disciplines and complicate the evaluation of management interventions and their hydroclimatic feedbacks.
Here, we evaluate the performance and highlight the wide range of applications of Climate-CWatM (C-CWatM), a newly developed flexible modelling tool for simulating water resources management and river routing. C-CWatM uses land-surface model outputs as inputs and provides a coupling interface designed for quick integration with existing climate and Earth system models. We force C-CWatM using raw land-surface outputs obtained from high-resolution regional climate model simulations across the EURO-CORDEX domain. To evaluate its performance, we compare simulated discharge between 1990 and 2010 with observed data from medium-sized European river basins. Our findings indicate reasonable performance, even when using raw, non-bias-corrected, unconstrained climate model output for runoff and other land-surface variables as input. We further evaluate the performance of C-CWatM against dedicated hydrological simulations using the offline hydrological model CWatM, driven by tailored, bias-corrected forcing datasets. The results demonstrate a strong agreement in both spatial and temporal discharge patterns, highlighting the effectiveness of C-CWatM in hydrological and water resources simulation for integration with climate models.
Due to its flexible, open-source, and accessible design, C-CWatM represents a critical step towards fully coupled modelling of climate–water–human interactions. The implementation of a coupled modelling system that includes C-CWatM can close the gap between water management, hydrology, and land–atmosphere interaction, supporting more consistent assessments of future water availability, hydroclimatic extremes, and the associated adaptation strategies.
How to cite: Greve, P., Schmitt, A., and Schreiber, S.: Evaluating a hydrological modelling tool for integration with climate models across Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17593, https://doi.org/10.5194/egusphere-egu26-17593, 2026.
The historical climatic data provide valuable information to understand the groundwater behavior. When groundwater and surface data levels are combined with climatic records, the water levels present the influence of El Niño–Southern Oscillation. However, there is no comprehensive record of surface and groundwater levels in Mexico, which limits this focus. This is the first study to evaluate the influence of the ENSO on hydrogeological dynamics in a groundwater flow system (GFS) placed in Central Mexico. The methodology consisted of compiling groundwater and surface water levels from multiple sources and data sets of historical time series of precipitation, runoff, and spatial/temporal variability patterns across different ENSO phases. The main results indicate that precipitation and surface runoff exhibit a strong response to El Niño and La Niña events, resulting in distinct hydrological anomalies that impact the recharge and discharge dynamics of the basin. Indicators show decreases in precipitation and groundwater levels during El Niño events, and increases in precipitation and surface water levels during La Niña events.
Multidecadal trends indicate that land use and vegetation changes significantly modify the hydrological response to ENSO by intensifying evapotranspiration, altering infiltration rates, and affecting the interaction between groundwater and surface water. These analyses allow us to understand the complex relationship between historical climate data and water levels, linked to natural processes and anthropogenic processes, especially those associated with water extraction.
This study provides an example for evaluating climate and hydrological changes linked to anthropogenic activities to improve sustainable management of water resources.
How to cite: Olea Olea, S., Medina-Ortega, P., Sierra García, B. A., Salgado-Albiter, A. C., Ramírez-González, L., Morales-Casique, E., and Ramírez Serrato, N. L.: Historical climatic time series analysis of ENSO influence on surface and groundwater levels in Central Mexico, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3715, https://doi.org/10.5194/egusphere-egu26-3715, 2026.
Abstract
Extreme low flow is a defining aspect of river regimes, posing significant risks to water management through reduced water availability and deteriorating water quality. Reliable estimates of design low flows for given non-exceedance probabilities are therefore essential. Traditional low-flow frequency analysis assumes independent and identically distributed (i.i.d.) data, an assumption increasingly violated under climate change and by distinct summer-winter generation processes. In snow-influenced climates, annual low flows can arise from events in both seasons with potential seasonal dependence, that challenges conventional models. This study extends traditional low-flow frequency analysis to non-stationary conditions by jointly accounting for temporal trends, process heterogeneity, and seasonal dependence. Building on the mixed distribution and mixed copula frameworks of Laaha (2023a, 2023b), the approach is extended to non-stationary conditions using the three-parameter Weibull distribution, allowing the seasonal low-flow distributions to change over time. The resulting models are evaluated across the European Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) dataset. Results indicate that neglecting non-stationarity when present can misrepresent low-flow severity, particularly for longer return periods. By preserving the conceptual consistency of the previous stationary modelling framework, the proposed non-stationary framework improves the statistical description of extreme low-flow events and provides an enhanced basis for low-flow frequency analysis, offering new insights into past and current low-flow behaviour under climate change.
Keywords: Non-stationary frequency analysis, low flow, drought, climate change, seasonality
Reference
Laaha, G. (2023a). A mixed distribution approach for low-flow frequency analysis – Part 1: Concept, performance, and effect of seasonality. Hydrol. Earth Syst. Sci., 27(3), 689-701. https://doi.org/10.5194/hess-27-689-2023
Laaha, G. (2023b). A mixed distribution approach for low-flow frequency analysis – Part 2: Comparative assessment of a mixed probability vs. copula-based dependence framework. Hydrol. Earth Syst. Sci., 27(10), 2019-2034. https://doi.org/10.5194/hess-27-2019-2023
How to cite: Fitriana, F. S., Fischer, S., Weigelhofer, G., Laimighofer, J., and Laaha, G.: Non-stationary low-flow frequency analysis with mixed Weibull components and Copula-based dependence framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1750, https://doi.org/10.5194/egusphere-egu26-1750, 2026.
Climate change is altering the global hydrological cycle and, when combined with human interventions such as reservoir operations, the river flow regime is further modified. Given the strong spatial heterogeneity of these impacts and the basin-specific nature of hydrological responses, regional studies are essential to assess local vulnerabilities. This study investigates projected changes in streamflow in the upper Miño River basin (northwestern Iberian Peninsula), including the impact of the Belesar reservoir, by comparing historical conditions (1985–2014) with future projections (2070–2099) under the SSP5-8.5 and SSP2-4.5 scenarios. Artificial neural networks were employed to model basin hydrology by estimating streamflow from temperature and precipitation data, and to simulate reservoir operations, achieving satisfactory validation performance.
Under the high-emission SSP5-8.5 scenario, results indicate a projected intensification of hydrological variability, with the 10th percentile, used to define low-flow conditions, decreasing by approximately 10%, whereas the percentile corresponding to a one-year return period (high-flow conditions) increases by about 5%, with the mean streamflow declining by more than 15%. Under the more moderate SSP2-4.5 scenario, changes are less pronounced, with a ~5% reduction in the low-flow percentile and a more moderate decrease in mean streamflow, while the high-flow percentile is expected to decrease by around 30 %, exhibiting an opposite trend to the extreme emission scenario. Reservoir operation was analysed under the SSP5-8.5 scenario to assess its regulatory capacity under future extreme conditions. Results show that reservoir management could mitigate projected impacts by redistributing water seasonally, more than doubling summer downstream flows compared to future natural conditions and reducing winter extremes, with peak flows lowered by approximately 15%. Overall, while future natural conditions are projected to become more critical, both moderate emission pathways and effective reservoir operation can substantially alleviate adverse hydrological impacts.
How to cite: Barreiro-Fonta, H. and Fernández-Nóvoa, D.: Neural Network Modelling of Climate Change and Reservoir Impacts on Upper Miño River Flow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20172, https://doi.org/10.5194/egusphere-egu26-20172, 2026.
Posters on site: Thu, 7 May, 16:15–18:00 | Hall A
Sub-Saharan Africa (SSA) is increasingly exposed to unprecedented climate extremes, posing critical challenges to water and food security. Hydrological and agricultural climate-change impact assessments commonly rely on downscaled and bias-corrected climate model simulations to drive hydrological and sectoral impact models. In many regions, including Sub-Saharan Africa, existing studies predominantly apply bias correction to single realisations from multi-model climate ensembles, which limits the explicit sampling of internal climate variability and constrains robust quantification of climate-change impacts and associated uncertainty. To account for internal variability in regional climate change projections, single model initial-condition large ensembles (SMILEs) can be used. Across diverse case studies in Europe and North America, different approaches have been developed to downscale and bias-correct SMILEs while preserving internal climate variability. However, these approaches have so far been applied almost exclusively to individual SMILEs, have not been extended to multiple SMILEs within a unified bias-correction framework, and remain unexplored in the SSA context.
This study presents the first multi-model, bias-corrected large-ensemble for high-resolution climate impact assessment in Sub-Saharan Africa, using Uganda as a demonstrative case study. The framework integrates six CMIP6 SMILEs (MPI-ESM1-2-LR, ACCESS-CM2, IPSL-CM6A-LR, MIROC6, CanESM5, and UKESM1-0-LL), together providing more than 150 climate simulations sampling both internal climate variability and inter-model structural uncertainty. Bias correction is applied at monthly scale using the CDF-t method, following the ensemble-based and individual-member-based implementations proposed by Ayar et al. (2021). The correction functions are trained over the historical period 1950–2014, using ERA5-Land as the reference dataset, resulting in bias-corrected regional climate scenarios at 8 km spatial resolution.
The resulting bias-corrected multi-model large ensemble is intended for use in hydrological and agricultural impact modelling over selected Ugandan catchments to support future analyses of hydroclimatic change, variability, and extremes. Beyond this case study, the framework is designed as a scalable prototype for the future development of a pan-SSA multi-model, bias-corrected large-ensemble climate dataset to support climate-impact assessments and adaptation planning.
How to cite: Dieppois, B., Oramah, S., Ekolu, J., Onyutha, C., Rubinato, M., and Van De Wiel, M.: A Multi-model Bias-corrected Large-Ensemble for High-resolution Climate Impact Assessment in Sub-Saharan Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20177, https://doi.org/10.5194/egusphere-egu26-20177, 2026.
The likelihood of unprecedented precipitation extremes is increasing across Sub-Saharan Africa (SSA), yet where and when future events may exceed the full range of historical experience remains understudied. While previous studies have documented historical trends and projected changes in precipitation extremes across sub regions of Africa, integrated SSA-wide assessments explicitly identifying hotspots of record-breaking precipitation extremes remain limited.
Here, we present a sub-continental, SSA-wide assessment of the emergence of unprecedented precipitation extremes under future climate change. Unprecedented extremes are defined as future events (2030-2100) that exceed the observed and simulated range during a historical reference period (1950-2014). Using precipitation-based extreme metrics relevant to water security (e.g., maximum 1-day rainfall, maximum 5-day rainfall, consecutive wet and dry days, maximum and minimum seasonal rainfall amount), derived from Coupled Model Intercomparison Project – Phase 6 (CMIP6) multi-model large-ensembles, we explicitly assess the future time and regional hotspot of emergence of record-breaking precipitation conditions. We also examine changes in the probability of emergence of unprecedented extremes and their potential large-scale ocean-atmospheric drivers (e.g., El Nino-Southern Oscillation, Atlantic Multidecadal Variability, and Indian Ocean Dipole), while accounting for uncertainties associated with both model physics and internal climate variability.
By systematically identifying where and when observed and retrospectively simulated precipitation limits are exceeded, this study offers a new sub-continental perspective on the emergence of unprecedented hydroclimatic conditions and provides a robust foundation for assessing future water security risks and supporting climate-resilient planning in Sub-Saharan Africa under increasing hydroclimatic uncertainty.
How to cite: Oramah, S., Dieppois, B., Ekolu, J., Onyutha, C., Stecher, G., Nkwasa, A., Diop, S. B., Tramblay, Y., Sultan, B., Northey, J., and van de Wiel, M.: Identifying hotspots for the emergence of unprecedented precipitation extremes in Sub-Saharan Africa under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12824, https://doi.org/10.5194/egusphere-egu26-12824, 2026.
The increasing frequency of short-duration, high-intensity rainfall events enhances the risk of flash floods, particularly in urbanised and low-lying areas where drainage systems are heavily loaded. In response to the need for rapid hazard assessment, simplified modeling tools are increasingly applied to provide fast estimates of flood inundation. The aim of this study is to verify whether simplified flash flood modeling performed using the Scalgo Live environment can be considered a reliable tool for preliminary flood risk analysis.
The analyses were conducted for a low-lying catchment located in southern Poland, characterized by a complex drainage system consisting of open channels and melioration ditches. Simulations were performed for intense short-duration rainfall scenarios with a probability of occurrence of p = 1%, as well as for variants including the implementation of a retention basin. Results obtained with Scalgo Live were subsequently verified using the hydrodynamic SWMM model.
The results indicate a significant increase in inundation extent and water volume with increasing rainfall duration. The flooded area increased from approximately 4.5 ha for a 15-minute rainfall event to more than 15 ha for a 24-hour event, while the volume of stagnant water rose from about 9.6 × 10³ m³ to over 4.2 × 10⁴ m³. The largest inundation extent was observed for the 24-hour rainfall scenario . Scalgo Live enabled a clear identification of critical sections of the drainage system where hydraulic capacity was exceeded. Comparison with the SWMM model showed good agreement in the location of inundated areas and hydraulic overloads. The implementation of a retention basin resulted in a clear reduction of inundation extent. The results confirm that Scalgo Live is a useful tool for rapid, preliminary flash flood risk assessments.
How to cite: Więzik, B. and Wałęga, A.: Verification of simplified flash flood inundation modeling using Scalgo Live and the SWMM model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2955, https://doi.org/10.5194/egusphere-egu26-2955, 2026.
Climate change is progressively altering the hydrological regime of Mediterranean coastal regions, with direct implications for groundwater recharge and the vulnerability of coastal aquifers to saltwater intrusion. This study assesses changes in the hydrological balance of south-eastern Sicily, with a focus on the Ragusa province, adopting a regional-scale approach rather than a single-basin analysis.
Historical climate data and future projections of temperature and precipitation were analysed to estimate the spatial and temporal evolution of the main components of the hydrological balance. Results indicate a marked decrease in effective precipitation, together with increasing temperatures and evapotranspiration. Under the high-emission RCP8.5 climate scenario, regional-scale groundwater recharge is projected to decline by approximately 40–45% from 2071–2100 relative to 1971–2000, with substantial spatial variability.
The strongest reductions are observed in coastal and low-lying areas, where the diminished freshwater input may significantly affect aquifer equilibrium. Such a deterioration of the regional hydrological balance represents a critical predisposing factor for saltwater intrusion, particularly in areas already subjected to intense groundwater abstraction.
These findings highlight the relevance of regional-scale hydrological balance assessments for identifying areas of increased vulnerability and for supporting sustainable groundwater management strategies in Mediterranean coastal environments under changing climatic conditions.
How to cite: Barone, S., Sebastiano, I., and Luca, C.: Regional-scale assessment of climate-driven hydrological balance changes and implications for coastal aquifer recharge in south-eastern Sicily, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9509, https://doi.org/10.5194/egusphere-egu26-9509, 2026.
Climate change poses an escalating threat to global water resources, with semi-arid regions such as Morocco being particularly vulnerable due to high climatic variability and limited adaptive capacity. In these regions, data scarcity and uncertainties related to data availability and quality frequently hinder robust assessments of climate change impacts. Recent advances in data science and remote sensing offer promising alternatives to overcome these limitations. This study investigates the potential of PERSIANN-CDR satellite-based precipitation product, for assessing climate change impacts on water resources. The capability of PERSIANN-CDR to reproduce observed precipitation patterns and associated hydrological responses is evaluated through a comparative analysis using observed precipitation data. Results indicate that PERSIANN-CDR generally underestimates peak precipitation events and total rainfall amounts compared to in-situ observations. Runoff is simulated using two hydrological approaches: the GR2M conceptual rainfall–runoff model and the Thornthwaite climatic water balance method, both driven by observed meteorological data and PERSIANN-CDR precipitation.
Furthermore, climate change impacts are quantified using future climate projections from 5 climate models, under two scenarios: RCP4.5 and RCP8.5 for the periods 2030-2060 and 2061-2090. Changes in key hydrological indicators, including precipitation, runoff, and water balance components, are analyzed for both observation-based and satellite-based simulations. Results consistently show a marked temperature increase of 2–3 °C across all models, accompanied by a general decline in precipitation ranging from -40% to -80%, despite notable inter-model variability. These climatic changes translate into substantial reductions in runoff, with stronger decreases projected under the high-emission scenario and during the dry season. Monthly analyses reveal pronounced seasonal contrasts, highlighting the increased sensitivity of low-flow periods to climate forcing. Overall, surface water availability is projected to decrease by -60 to -80% (GR2M) and -70 to -80% (Thornthwaite) when using observed data, and by -50 to -80% (GR2M) and -50 to -90% (Thornthwaite) when using PERSIANN-CDR forcing. The results highlight the strengths of satellite-based precipitation datasets for climate change impact studies and demonstrate their relevance as a complementary or alternative data source in regions with sparse observations.
How to cite: Elomari, S., El Khalki, E. M., Nait-Taleb, O., and Elaloui, A.: Modeling Hydrological Responses to Climate Change in Morocco’s Upper Tassaoute Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7215, https://doi.org/10.5194/egusphere-egu26-7215, 2026.
Over the past few decades, extreme hydrological events, particularly floods and droughts, have increased across the European Alps.
Changes in the frequency and duration of wet and dry extremes may complicate reservoir management by intensifying tradeoffs among water supply, flood control, and ecosystem needs. Prolonged droughts can limit the ability to maintain minimum release requirements, while increased precipitation may raise storage levels and flood risks.
In this study, we present a preliminary assessment of changes in the frequency and duration of wet and dry extreme events in two anthropized, snow-dominated catchments of the upper Po River basin, with a specific focus on variations in reservoir inflows. The aim is to improve understanding of upstream streamflow variability and to support future reservoir and watershed management.
We use the GEOframe hydrological modeling system to simulate the complete hydrological cycle including snow water equivalent, soil moisture, and river discharge at ~1km2 - daily resolution. We exploit the potential of recently developed meteorological datasets for rainfall and air temperature covering the study area over the past 30 years. Model simulations are calibrated using historical streamflow observations and validated through both in situ data and independent validation based on MODIS MOD10A2 satellite observations of snow-covered areas.
This modeling effort provides insights into historical hydrological changes in hydrological extreme events, particularly those affecting inflow discharges to the analyzed reservoirs, and establishes a foundation for future analyses of projected hydrological changes and reservoir operation over a changing environment.
This work is supported by the project WATER4ALL JTC2022” - WaterMA-WaDiT - “Water Management and Adapation based on Watershed Digital Twins” CUP: E63C23001680007
How to cite: Formetta, G., Pianosi, F., Busti, R., Andreis, D., Roati, G., Pimentel, R., Rigon, R., and Del Jesus Penil, M.: Climate-driven historical changes in streamflow extremes and consequences for reservoir inflows over the Upper Po river basin (Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11519, https://doi.org/10.5194/egusphere-egu26-11519, 2026.
The Ganga River basin is home to over 600 million people and holds significant economic and cultural importance. However, the Ganga River is experiencing a recent drying trend, threatening both water and food security. Using streamflow reconstructions spanning 1,300 y (700–2012 C.E.) from instrumental data, paleohydrological records, and hydrological modelling, we show that recent drying from 1991 to 2020 is unprecedented in the past millennium. Streamflow decline since the 1990s, driven by frequent and prolonged droughts, is 76% more intense than its closest historical analogue of the 16th-century drought. This drying exceeds natural variability, highlighting the dominant role of anthropogenic factors. Despite CMIP6 models projecting increased streamflow under warming scenarios, the recent decline indicates complexities associated with future water availability projections. Our findings underscore the urgent need to examine the interactions among the factors that control summer monsoon precipitation, including large−scale climate variability and anthropogenic forcings. Better constraints on these processes in climate models will be essential for improving future monsoon projections and implementing adaptive water management strategies to secure the Ganga basin’s freshwater availability under a changing climate.
How to cite: Singh Chuphal, D. and Mishra, V.: Unprecedented drying of the Ganga River over the past 1,300 years, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16194, https://doi.org/10.5194/egusphere-egu26-16194, 2026.
Extreme precipitation is growing more frequent and severe in many regions worldwide, increasing risks to lives, infrastructure, and agricultural production. Nowhere is this challenge more pronounced than in Africa, where rainfall is substantially erratic, and populations are frequently subject to droughts, floods, and sudden changes in seasonal patterns. Although previous studies have explored the links between climate and rainfall in Africa, most use simplified methods that smooth over regional differences and cannot capture how specific drivers influence different types of extremes. To address the gap, an explainable AI (XAI) framework was designed to predict ten standard ETCCDI precipitation extreme indices at grid-cell resolution across Africa, informed by 15 major climate drivers from the Pacific, Indian, and Atlantic oceans. The approach is based on a broadcast-fusion XAI-CNN that incorporates scalar climatic indices with spatial precipitation patterns. The model learn how various large-scale factors influence local extreme behavior, expanding each climate driver into a spatial layer. The model is developed using CHIRPS data from 1981 to 2025, achieving a mean R² of 0.80 throughout all indices, with the highest performance for PRCPTOT (0.90) and CDD (0.88). The results exhibit that the large-scale drivers contain sufficient insight to predict wet and dry extremes at the continental scale. The findings challenge the long-standing view that ENSO is the most dominant influence on African rainfall. However, the Tropical Atlantic emerges as the strongest driver of extreme wet events, while the Indian Ocean Dipole, central Pacific ENSO, and Pacific Warm Pool exhibit regionally specific influences on East, Southern, and Central Africa, respectively. The study presents a transparent and scalable approach to characterizing hydroclimatic extremes by integrating deep learning with an explainability framework. The spatial explainability analysis improves interpretability and reveals physically consistent teleconnection patterns, opening avenues for regionalized climate prediction and disaster risk reduction.
How to cite: Berama, S. M. A. and Kasiviswanathan, K. S.: A Novel Multi-Driver Explainable AI (XAI) Framework for Predicting African Precipitation Extremes at Grid-Cell Resolution: Insights from 15 Climate Drivers over Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-969, https://doi.org/10.5194/egusphere-egu26-969, 2026.
The Caatinga biome, located in northeastern Brazil, is a semi-arid region highly exposed to hydroclimatic variability, recurrent droughts, and increasing thermal stress. As the driest and socioeconomically most vulnerable region of the country, robust assessments of climate extremes are essential to support adaptation and resilience planning. This study investigates historical climate extremes and future projections over the Caatinga using a performance-based subset of three bias-corrected global climate models from the NEX-GDDP-CMIP6 dataset: CESM2, TaiESM1, and MRI-ESM2-0.
The historical evaluation covers the period 1981-2014 and is based on gridded observations and reanalysis data. ERA5 exhibits good agreement with observations for percentile-based temperature indices (TN10p, TN90p, TX10p, TX90p) and the Warm Spell Duration Index (WSDI). However, large Percent Bias values (>80%) are identified over the São Francisco River Basin, indicating regional discrepancies. For precipitation extremes, the R20mm frequency index reveals dominant drying trends in the same basin, highlighting a regional hotspot of hydroclimatic stress.
Observed extremes show a clear intensification of hot events, while increasing consecutive dry days (CDD) exacerbate drought impacts across the Northeast. The northern Caatinga and the central-southeastern sector associated with the São Francisco Basin exhibit consistent drying signals, despite an increase in the frequency of extreme precipitation events since the 1980s. In contrast, coastal areas show a reduction in the frequency of hot days, alongside a general decline in annual precipitation totals and extreme rainfall frequency across most of the Caatinga.
Future projections are analyzed for near-term (2021-2040), mid-term (2041-2060), and long-term (2081-2100) periods, indicating a substantial reduction in total precipitation and an intensification of compound heat-dryness extremes. These changes pose severe risks to water availability, ecosystem stability, and human livelihoods, threatening millions of people and reinforcing the urgency of climate adaptation policies in semi-arid regions.
How to cite: Prestrelo de Oliveira, C., Rodrigues Mutti, P., Nunes Cho-Luck, E., Siqueira, M., Batista, G., Ferreira Costa, R., Leidinice da Silva, M., Jeferson de Medeiros, F., and Lu Aramayo Alonso, W.: Climate Extremes over the Brazilian Caatinga Based on Performance-Based Projections from Selected NEX-GDDP-CMIP6 Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3469, https://doi.org/10.5194/egusphere-egu26-3469, 2026.
Drought is expected to intensify under climate change, leading to increasing impacts on society and ecosystems. Well-informed preparedness against these changes is confronted by substantial uncertainty in regional climate responses, as different Global Climate Models (GCMs) produce a wide range of changing signals under the same emission scenario. Multi-model means are commonly used to address this uncertainty which may mask the inter-model variability, and in some cases, the opposing signals across models further reduce the overall change, thereby limiting the risk exploration. Recent work suggests that clustering GCMs based on local impact drivers can improve the representation of plausible future climates and their associated extremes. In this study, we apply the climatic impact-driver (CID) clustering approach to explore future drought risk in the Guadalquivir River Basin, Spain. Both hydrological and agricultural drought were quantified using outputs from the wflow_sbm model and crop water requirements. Seasonal CMIP6 changes in precipitation and potential evapotranspiration (PET) were analyzed using the random forest scoring technique to identify the dominant climatic drivers for drought impact. Our results indicate that winter and autumn precipitation deficits are the main drivers of streamflow drought, while winter increases in PET act as a secondary driver of extreme and multi-year hydrological drought. In contrast, summer and spring increases in PET emerge as the dominant driver of agricultural drought. Based on these identified drivers, we are going to cluster the GCMs for different future horizons to compare the resulting impact ranges with traditional emission-based ensembles. This ongoing research suggests that drought-specific clustering provides a more informative set of impact scenarios than SSPs. As such it supports robust adaptation planning for water managers under uncertain climate change impacts in Mediterranean river basins.
How to cite: Mohammed, ., Buskop, F. E., Sperna Weiland, F., and Teuling, A. J.: Better Exploration of Drought Risks Under Climate Change Uncertainties using Locally Relevant Climatic Drivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18656, https://doi.org/10.5194/egusphere-egu26-18656, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot A
EGU26-14233 | Posters virtual | VPS9
Flash Flood Events in the Northwestern Black Sea Region under Climate ChangeWed, 06 May, 14:51–14:54 (CEST) vPoster spot A
Extreme hydrological events have become increasingly frequent in Ukraine in recent decades due to climate change and structural weaknesses in water resources management. According to the Water Strategy of Ukraine up to 2050, inadequate governance practices remain a major source of anthropogenic pressure on water bodies, while climate change creates additional risks through prolonged droughts interrupted by intense rainfall events, leading to flooding. These challenges are particularly critical for southern Ukraine, where limited water resources require extensive hydrotechnical regulation and adaptive management.
Flash floods represent one of the most dangerous manifestations of hydrological extremes. Characterised by rapid water-level rise and high flow velocities, they pose severe risks to settlements, infrastructure, and agriculture due to their sudden onset.
The north-western part of the Black Sea region has experienced several severe flash flood events over the past decade. One of the most significant cases occurred in September 2013 in the Kogilnyk River basin., when anomalously high precipitation totals of 41 - 270 mm were recorded from 10 and 14 September. These extreme rainfall conditions were associated with a stationary cold atmospheric front linked to the Asia Minor depression, resulting in prolonged convective rainfall with thunderstorms, squalls and wind gusts of up to 22 m/s in the southern districts of the Odesa region.
The total volume of storm rainfall during this event is estimated at approximately 250 million cubic meters, which exceeded the mean annual runoff of the Kogilnyk River by a factor of 5.5. Precipitation affected an area of about 1,400 km², corresponding to 35% of the total river basin area. As a result, flash flooding impacted multiple settlements, located in the south-western part of Odesa Oblast as well as extensive agricultural lands in there.
Another notable episode occurred in early August 2019, when unstable atmospheric conditions and active cyclones caused intense rainfall across southern and eastern Ukraine. On 3 - 4 August, precipitation amounts reached 130–220% of the monthly norm in several locations. In the Odesa region, rainfall totals of up to 126 mm - equivalent to nearly three months of precipitation—met the criteria for hazardous meteorological phenomena and triggered debris flows and localized flash flooding, particularly in the village of Moloha (Bilhorod-Dnistrovskyi district).
More recently, in September 2025, an urban flash flood in Odesa highlighted the increasing vulnerability of urban areas to extreme rainfall. Prolonged heavy rains caused widespread flooding, significant damage, and human losses, prompting large-scale rescue operations..
The analysed events indicate a clear increase in flash flood intensity and impacts in the north-western Black Sea region. Under continued climate change, enhanced hydrological monitoring, early warning systems, climate-adaptive urban planning, and integrated water resources management are urgently required in southern Ukraine.
ACKNOWLEDGEMENTS
This contribution builds on the conceptual framework of the applied research project “Sustainable Development of Water Resources Management and Modelling in the North-Western Black Sea Region under Conditions of Increasing Climate Extremes and Anthropogenic Pressure”, approved for funding by the Ministry of Education and Science of Ukraine (Order No. 23, 9 January 2026, see https://surl.li/omqxph).
How to cite: Ovcharuk, V. and Khomenko, I.: Flash Flood Events in the Northwestern Black Sea Region under Climate Change , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14233, https://doi.org/10.5194/egusphere-egu26-14233, 2026.
EGU26-9421 | ECS | Posters virtual | VPS9
Asymmetric intensification of nighttime versus daytime precipitation extremes under warmingWed, 06 May, 15:15–15:18 (CEST) vPoster spot A
The intensification of the global hydrological cycle is a well-established consequence of anthropogenic climate change. However, how this intensification manifests across the diurnal cycle remains poorly understood, representing a critical blind spot in climate risk assessments. While daily-aggregated metrics consistently suggest a "wetter and more extreme" climate, they mask fundamentally different responses of daytime and nighttime precipitation to warming.Here we analyse high-resolution observational records from 2,399 stations across China spanning 1972–2024 and identify a distinct nighttime intensification regime that is increasingly dominant under warming. In regions experiencing active wetting, extreme precipitation (R95p) intensifies more rapidly at night than during the day, both in magnitude and spatial extent.This diurnal asymmetry reflects contrasting physical controls. Nighttime wetting is driven almost exclusively by increases in precipitation intensity (p < 0.001, Wilcoxon signed-rank test) and exhibits a tight thermodynamic scaling with background warming. By contrast, daytime precipitation changes arise from a heterogeneous combination of intensity and frequency adjustments, indicating a greater role for dynamical modulation.
These findings reveal a previously underappreciated amplification of nocturnal hydrometeorological hazards, including flash floods and landslides, that is systematically underestimated by daily-mean indicators. As global warming continues, the emerging dominance of nighttime precipitation extremes underscores the urgent need to incorporate diurnally resolved processes into climate risk assessment, infrastructure design and early-warning systems.
How to cite: Meng, J. and Xia, H.: Asymmetric intensification of nighttime versus daytime precipitation extremes under warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9421, https://doi.org/10.5194/egusphere-egu26-9421, 2026.
