Orals: Mon, 4 May, 14:00–18:00 | Room 3.29/30
In Autumn of 2000, intense rainfall occurred in the Alpine regions of the Po River basin between 13 and 16 October. The resulting flood wave reached Pontelagoscuro — conventionally considered the basin outlet — on 20 October, when the river discharge peaked at more than 13,500 m³/s, one of the highest values ever recorded. Average rainfall over the 70091 km2 Po River catchment was about 162 mm.
The total mechanical energy released by the rainfall mass over the land surface during 13–16 October, relative to the mean sea level and accounting for both the potential and kinetic energy of raindrops, amounts to approximately 0.13 exajoules. This amount of energy is comparable to more than eight years of electricity consumption by a large metropolitan area such as New York City and corresponds to roughly 2,000 times the energy released by the Hiroshima atomic bomb. While these comparisons do not imply a strict physical equivalence, they provide a framework for contextualizing the magnitude of the energy involved in extreme precipitation and flood-generating processes, and help to explain the destructive potential of flood events, as demonstrated by several recent cases. Consistent with this interpretation, the EM-DAT International Disasters Database reports that the Po River flood in 2000 resulted in 25 fatalities, affected approximately 43,000 people, and caused total economic losses of about 8 billion US dollars (2000 value).
A large fraction of the energy associated to extreme rainfall events is dissipated as heat through friction during surface runoff and river flow, while simultaneously driving hillslope and riverbed erosion and sediment transport, processes that may in turn enhance the overall energy of the flood. Another portion of the energy is temporarily stored within the catchment, particularly in artificial reservoirs, and released at later stages. Part of the energy is conveyed along the river channel and, under ordinary conditions, does not produce significant impacts because it remains confined to areas of low exposure, such as the riverbed and adjacent floodplains.
Flood impacts arise when the trajectories of energy fluxes (i.e. power) intersect with people and societal assets, namely when water spills out from the river bed and spreads into highly exposed areas. Under specific flow conditions, the power associated with the flooding water can increase substantially, leading to a marked amplification of impacts—for example, when floodwaters enter urban streets and vehicles are entrained and transported downstream due to high local power, or when energy accumulates and is subsequently released abruptly. Another reason for impact amplification is associated to the conversion of energy flux into the rate at which damage, disruption, or harm propagates through a human–environment system during a flood. Consequently, the analysis of energy and impact fluxes represents an essential tool for modeling and predicting compound events, flood damage and potential destruction, and designing strategies to increase resilience.
We present a workflow grounded in dynamical systems theory for analyzing, modeling, and predicting the trajectories of energy, power and impact fluxes during flood events, for identifying critical situations for flood impact amplification.
How to cite: Guo, R., Bloeschl, G., and Montanari, A.: The energy of floods: an overlooked perspective on flood impact amplification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9093, https://doi.org/10.5194/egusphere-egu26-9093, 2026.
Drought is a complex and persistent hazard affecting agriculture, ecosystems, economic stability, and public health. Traditional univariate drought indices often overlook the interconnected behavior of drought components, limiting their capacity to support holistic drought assessment and early warning. To address this gap, we develop a Bayesian Copula-Based Integrated Drought Index (IDI) that jointly represents meteorological, hydrological, and agricultural drought conditions across India. The framework integrates a modified Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSMI) at multiple monthly timescales using gridded data at 0.25° resolution from 1951 to 2024. An Archimedean copula family is used to characterize the dependence structure among drought drivers. Marginal distributions are selected based on a rigorous comparison of candidate probability models; Gamma for precipitation and GEV for both streamflow and soil moisture, as validated through the Kolmogorov–Smirnov test and Akaike Information Criteria. Model parameters are estimated through Bayesian inference via the Differential Evolution Markov Chain (DE-MC) algorithm, which combines differential evolution with Markov Chain Monte Carlo sampling to ensure robust, efficient convergence and uncertainty quantification. Comparative analysis demonstrates that the IDI outperforms individual indices in representing the spatial extent, persistence, and severity of drought events. By accounting for multi-source drought information within a probabilistic and dependency aware framework, the proposed IDI advances compound drought monitoring capabilities and supports more informed climate adaptation and water management strategies. This approach significantly enhances understanding of drought dynamics and provides policymakers and stakeholders with a stronger decision-support tool amid increasing climate variability.
How to cite: Mohseni, U. and Rajendran, V.: A Bayesian Copula Based Integrated Drought Index for Compound Drought Monitoring in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-751, https://doi.org/10.5194/egusphere-egu26-751, 2026.
Stochastic simulations have been widely applied in water-related risk management, particularly for estimating annual Net Basin Supplies (NBS) in the Lake Champlain–Richelieu River (LCRR) basin. Following the unprecedented flood event in 2011, simulated NBS datasets were required to re-evaluate existing flood-protection infrastructure and to support the development of future mitigation strategies within the basin. Because water-resources operation and flood-management planning are typically conducted at monthly or quarter-monthly resolutions, the simulated annual NBS data must be disaggregated to finer temporal scales. In this study, several existing disaggregation approaches were applied to the simulated annual NBS series, with the objective of reproducing the key statistical characteristics associated with the 2011 flood event in the LCRR basin. The 2011 flood was characterized by its persistence over multiple months, indicating that an appropriate disaggregation framework must be able to maintain both interannual dependence and month-to-month temporal relationships in the resulting monthly series. The analysis shows that currently available parametric and nonparametric disaggregation models exhibit clear limitations, particularly in their ability to preserve sufficient temporal dependence. To address these deficiencies, this study proposes a new random block-based nonparametric disaggregation (RB-NPD) model. In addition, the proposed framework is further enhanced by incorporating a Genetic Algorithm–based mixture scheme to improve the representation of lagged correlations. The results demonstrate that the RB-NPD model provides a viable alternative to existing methods, and that its enhanced version is well suited for disaggregating annual NBS data in the LCRR basin.
How to cite: Lee, T., Kong, Y., and Yoon, Y.: A Random Block-Based Nonparametric Approach for Temporal Disaggregation of Net Basin Supplies in the Lake Champlain–Richelieu River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1978, https://doi.org/10.5194/egusphere-egu26-1978, 2026.
As climate change intensifies hydrological extremes, loss and damage (L&D) increasingly reflects not only the severity of hazards but also patterns of exposure, vulnerability, and the limits of adaptation. While recent research on hydrological extremes has advanced modelling of hazards and compound events, less attention has been paid to empirically linking risk profiles with observed loss and damage and lived adaptation responses. This study addresses this gap by applying a risk-based assessment framework to examine how flood risks translate into economic and non-economic loss and damage across districts in Assam, one of India’s most flood-prone states.
Building on the IPCC risk framework, flood risk is assessed as the interaction of hazard, exposure, and vulnerability using district-level indicators. Observed loss and damage is quantified using official disaster records from 2015–2023, disaggregated across housing, agriculture, livelihoods, infrastructure, and loss of life. The analysis empirically demonstrates that 24% of Assam’s districts fall within high flood-risk zones, experiencing substantial losses including infrastructure damage, loss of lives and livelihoods, and recurrent displacement. In these districts, repeated flooding forces households to abandon permanent homes and reside in temporary chang ghar (kutcha houses), often without secure livelihood options.
A further 61% of districts fall under moderate flood risk, where exposure and vulnerability - rather than hazard intensity - are the dominant drivers of loss and damage. These districts experience significant socio-economic impacts, including loss of life, livelihood disruption, and distress migration, with male household members frequently migrating to nearby districts or other regions as a coping response. The remaining 15% of districts are categorised as low flood risk, yet still experience livelihood-related loss and damage driven primarily by high vulnerability, indicating clear scope for targeted policy interventions to reduce residual risk.
To move beyond aggregated loss metrics, qualitative fieldwork in selected districts explores non-economic loss and damage, including health impacts, psychological distress, livelihood insecurity, cultural loss, and erosion of place attachment. The study further examines locally practised coping, incremental, and transformative adaptation strategies, revealing persistent mismatches between technocratic adaptation interventions and lived realities. Many losses persist despite adaptation efforts, underscoring adaptation limits and positioning loss and damage as a governance challenge rather than a purely technical one.
By empirically linking risk profiles, observed loss and damage, and adaptation practices, this study demonstrates how vulnerability-centred risk assessment can bridge adaptation planning and loss and damage policy, informing more equitable and context-sensitive climate responses in flood-prone regions.
How to cite: Barua, A. and Vyas, S.: Linking Flood Risk Assessment, Adaptation Limits, and Loss and Damage: Evidence from a Risk-Based Framework in Assam, India , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3593, https://doi.org/10.5194/egusphere-egu26-3593, 2026.
Research on compound dry and heat wave events (CDHWs) has been limited by inconsistencies in the temporal resolutions of their constituent hazards, with droughts commonly characterized at monthly scales and heat waves at daily scales. The development of daily-scale drought indices enables the identification of dry events on a daily basis, thereby facilitating more detailed investigations of CDHWs. Using daily Standardized Precipitation Evapotranspiration Index (daily-SPEI) data and heat wave records, CDHWs were identified for the period 1961–2020, and their spatiotemporal variations in frequency, duration, dry severity, and heat intensity were systematically analyzed. Extreme CDHWs were further defined based on the upper thresholds of dry severity and heat intensity across all identified events, and changes in their occurrence probabilities, along with the relative contributions of dry events and heat wave events, were examined.
The results indicate a widespread intensification of CDHWs across global land areas, with particularly pronounced increases in western North America, eastern South America, Europe, northern Africa, and parts of Asia. The frequency of CDHWs shows significant upward trends since the 1990s, with a marked acceleration in recent years. Notably, extreme CDHWs exhibit more severe changes during 1991–2020 compared with 1961–1990. Consequently, the return periods of extreme CDHWs have decreased significantly across nearly all global land regions, with reductions exceeding 60% in many areas. Control variable experiments further demonstrate that changes in heat wave events contribute more to the reduction in return periods of extreme CDHWs than changes in dry events, accounting for approximately 23%–63% and 6%–13%, respectively. Overall, this study advances the understanding of CDHWs at daily temporal scales and underscores the need to place greater emphasis on extreme compound events under rapidly intensifying climate conditions.
How to cite: Jiang, L., Zhang, J., Bai, L., Han, J., Meng, X., Cao, D., and Al-Sakkaf, A. S.: Widespread intensification of compound dry and heat wave events at daily scales over global land regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10891, https://doi.org/10.5194/egusphere-egu26-10891, 2026.
Numerous methods have been previously developed for design flood estimation. Where sufficient runoff data are available, statistical methods for flood frequency analysis are often the preferred approach. In cases where such data are scarce, methods involving hydrological simulation are an attractive alternative. Simulation methods range in complexity from the very simple, formula-based, Rational Method to the simulation of runoff using complex hydrological models also with stochastic input. In the simple models, the return period of runoff often inherits the return period from the input, i.e. the precipitation intensity for a given return period. In this case, arbitrary assumptions are often made regarding initial conditions, e.g. soil moisture states. Here, we investigate the relationship between extreme precipitation, precipitation sequences, initial soil moisture states and peak discharge to estimate extreme floods using hydrological simulations. We use the parameters and simulation results of the DDD (Distance Distribution Dynamics) hydrological model to parameterise an event-based model (DDDEvent) which is run for a range of precipitation intensities, precipitation sequences, and initial soil moisture states. When running the event model, a value of a specific precipitation intensity is used and initial soil moisture state and precipitation sequence are stochastically drawn from a gamma distribution and a beta distribution, respectively. This procedure is repeated for a range of precipitation intensities. The (simulated) initial soil moisture states are, in many catchments, found to be correlated with precipitation so we use a (gamma) distribution of antecedent soil moisture states conditioned on precipitation. Results show, expectedly, that varying the soil moisture state and precipitation sequence can give a range of runoff responses to a given precipitation input. When we simulate runoff for a single precipitation intensity and vary the soil moisture states and precipitation sequence, we obtain a conditional distribution of runoff, given the precipitation intensity. Similarly, for a simulated runoff value we find a range of possible precipitation intensities, and we obtain a conditional distribution of precipitation given the runoff value. From such (empirical) conditional distributions we can use Bayes’ theorem to assess the exceedance probability for a fixed value of runoff given the exceedance probability of the precipitation event. Simulation results using synthetic data show that the proposed approach is justified when runoff and precipitation are highly correlated, which is typically the case for extreme precipitation events. The approach is validated against extreme value estimates of floods using flood frequency analysis on long time series from the Norwegian Water Resources and Energy Directorate. Preliminary results for estimating instantaneous floods are promising for catchments where floods are primarily generated by extreme rainfall and snowmelt plays a minor role. The proposed method also has potential for estimating floods in ungauged catchments if reliable extreme value estimates of precipitation exist using a regionalised version of the DDD model.
How to cite: Skaugen, T., Lawrence, D., Engeland, K., and Fleig, A.: An event-based, Bayesian approach for estimating floods in urban and natural catchments , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13899, https://doi.org/10.5194/egusphere-egu26-13899, 2026.
A simple but robust depth-duration-frequency (DDF) model is presented to reveal the asymptotic characteristics of extreme but short-lived (sub-daily) precipitation events that satisfy a peak over threshold (POT) size criterion. Our objective is to reliably estimate the return periods for events of a given intensity (as measured by rainfall depth and duration).
For each depth threshold and duration period (ranging from 15 minutes to 24 hours), the number of qualifying POT events is simply counted over multi-year periods, whether from observations or model output, at each location separately. The distribution of events as a function of their size above the threshold is modelled by a generalized Pareto distribution (GPD), following standard extreme value theory. Those exceedance distributions are shown, to a good approximation, to be independent of location within Ireland. This justifies the aggregation of exceedances from multiple locations, which is a key feature of the model. Aggregation acts as a data multiplier, enabling more reliable estimation of GPD fits and return periods.
The model is applied to intense precipitation observations spanning 30–64 years at 23 stations in Ireland. Three-hourly output from an ensemble of CMIP5 global climate simulations, downscaled to high-resolution over Ireland, were also used to compute both historical and projected future intense event return periods under two different emission scenarios.
Future numbers of events per time-period are projected to increase by 20-80%, depending on event threshold and duration, location, emission scenario and time-period. Return periods are projected to shorten by factors of 2 or more for the most intense events, as illustrated by return period maps for events of any given size.
Return period uncertainty is quantified mainly by the spread among the different CMIP5 models. For any given model, however, robustness is demonstrated by the convergence of the empirical exceedance distributions as more stations (or grid-points) are aggregated, which then leads naturally to convergence of the GPD fits.
How to cite: O'Brien, E., Wang, J., Ryan, P., Nolan, P., and Mateus, C.: A Robust Depth-Duration-Frequency Model for Analysis of Extreme Precipitation Events, with Application to Past and Projected Future Climates in Ireland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15190, https://doi.org/10.5194/egusphere-egu26-15190, 2026.
Climate change intensifies the hydrological cycle leading to concerns in future water availability. The resulting changes in water availability need to be quantified to determine present and future actions needed with regards to water resources management. In this work, we focus on the soil moisture component of the hydrological cycle which is crucial for agriculture and ultimately for ensuring food security. We model future soil moisture levels under the high emissions RCP 8.5 scenario at 34 sites of the UK COsmic-ray Soil Moisture Observing System (COSMOS-UK) network. We do this by bringing together: the Joint UK Land Environment Simulator (JULES) land surface model, long-term field-scale soil moisture measurements from the COSMOS-UK network and 2.2 km convection-permitting UK Climate Projections (UKCP18). As a first step, we use the COSMOS-UK observations to optimise 12 parameters of the Cosby pedotransfer functions used in the JULES model. We then force the optimised JULES model with UKCP18 data to produce soil moisture estimates in three time periods: 1982-2000, 2022-2040 and 2062-2080. We interpret the results in the context of frequency of soil moisture drought events and the impact on individual months. We find that on average across all sites, there is an increase in future extreme soil moisture drought events above 90 days with respect to the historical period. In 2062-2080, the frequency of these events is expected to increase by a factor of between 1.8 and 2.8. We also show that months between May and November have an increased probability of high or more intense plant water stress in this far future period, with months between June and October being at especially high risk. This work has been published in https://doi.org/10.1088/1748-9326/ad7045.
How to cite: Szczykulska, M., Huntingford, C., Cooper, E., and Evans, J. G.: Future increases in soil moisture drought frequency at UK monitoring sites: merging the JULES land model with observations and convection-permitting UK climate projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16341, https://doi.org/10.5194/egusphere-egu26-16341, 2026.
The controlled drainage of diked hinterlands via sluices and pumping stations is a critical component of flood risk management in low-elevation coastal zones (LECZs), where floods are shaped by the interaction of rainfall, tides, storm surges, and sea-level rise. Effective drainage operation requires consideration of complex flood and tidal dynamics as drainage capacity is primarily impacted by the hydraulic gradient between inland water and downstream marine or estuary systems. As downstream water level dynamics dictate periods of gravity-driven drainage and the efficiency of pump operations, drainage capacity varies over time and depends heavily on tidal and storm surge conditions. Reduced drainage capacity significantly increases hinterland flood hazard, highlighting the importance of concurring and compounding events for flood risk management in LECZ.
To better understand the interaction of flood drivers in low-land drainage areas, we develop a statistical framework to describe the impact of seaside conditions on drainage capacity, focusing on gravity-driven drainage along the German North Sea coast. Using multi-decadal tidal gauge records, high-resolution digital elevation models, and site-specific inland control stages, we derive threshold-based drainage conditions at more than one hundred coastal catchment outlets. We define free-drainage periods as intervals with tidal water levels below the inland control stage and tidal low-water exceedance spells as periods during which consecutive tidal low waters remain above the control stage, preventing gravity-driven drainage processes completely. Based on these characteristics, we statistically analyse the impact of coastal water level conditions on drainage operation. Further, we link them to inland precipitation to analyse situations of increased compound flood hazard where rainfall coincides with reduced or precluded gravity-driven drainage using multivariate extreme value statistics.
Using this approach, we (i) define drainage condition metrics consistently across coastal drainage systems, (ii) quantify the duration, frequency, and temporal trends of compound flood hazard and (iii) demonstrate implications for the water management in LECZ.
How to cite: Müller, H., Engelmann, J., Jordan, C., Thierfeldt, J., Müller, N., David, G., and Schröter, K.: Analysis of drainage-dependent compound flood hazard along the German North Sea coast, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16836, https://doi.org/10.5194/egusphere-egu26-16836, 2026.
In the natural sciences, statistical learning for the geosphere time series (atmosphere, hydrosphere, cryosphere, lithosphere and biosphere) addresses the substance of uncertainty by treating the Earth as a coupled, multi-scale cyber system (J. Krcho, 1970). The time series are sequences of observation in time that reflect results of interaction subsystems of the Geosphere obtained as sequences of observation in time. The systemic approach shifts analysis from one dimensional time series to discover, describe and model complex interactions and cybernetics feedback loops across scales.
Statistical learning allows extraction of Hilbert Spaces from observations with results defining the Geosystems as fuzzy time-spatial structures (Zadeh) with dimensionality and quantitative characteristics of variability reflected from data. The substance of uncertainty may be defined as an ability for models to describe variability in data. From a natural scientist's perspective, uncertainty is not merely "noise" but a property of the systemic approach to modeling the Earth system's complexity.
The Hydrosphere is the most dynamic of the Geospheres and connects with all of them. The Hydrosphere may be described with nine interacting fuzzy elements: water of seas & oceans, stream runoff shell, water of closed lakes, atmospheric water, water of glaciers, water of permafrost rocks, connate groundwater, water trapped in rocks & minerals of lithosphere, and water of biosphere. The stream runoff shell includes* terrestrial stream network.
Model definition (Minsky, 1969) here includes a concept and kinds of coordinate system; a hierarchy of watersheds in the Hydrosphere; representation results of analysis of empirical data; representation of some* knowledge and new concepts.
Visualization results will be presented following the above concepts with interpretation on the example of two watersheds (USGS 04010500 PIGEON RIVER AT MIDDLE FALLS NR GRAND PORTAGE MN, USGS 06191500 Yellowstone River at Corwin Springs MT). Besides six models of these two mesoscale watersheds based on statistical learning of three types of fuzzy structures, the concept will be illustrated with reference to hydrological maps based on time spatial structure obtained by Statistical Learning with use of empirical data from the Great Lakes watershed of North America.
These models for watershed as an element of cyber model for Geosphere and results obtained them illustrates that the systemic approach with statistical learning on empirical data may be successful to find interactions for other Geospheres and bigger natural systems.
The Scientific Hydrology growled out from multiscale cartography surface and groundwater interaction for evaluating regional and global water resources (a Report by Gilbrich and Struckmeier "50 Years of Hydro(geo)logical Cartography", 2014 UNESCO CGWM IAN BGR) unfortunately as parallel branch to Stochastic Hydrology (Klemeš, Koutsoyiannis). The modern cartography of water resources taking root in concepts from Horton, Strahler, Kudelin, using statistical learning for quantitative description time spatial variability, is the scientific branch of Hydrology. Union of those two branches with joint efforts of scientists and engineers is certainly coming.
How to cite: Shmagin, B. and Krakauer, N.: The Substance of Uncertainty in Systemic Approach: Statistical Learning for Time Series of Geospheres: Natural Scientist's Point of View, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17737, https://doi.org/10.5194/egusphere-egu26-17737, 2026.
Flash droughts are defined by their unusually rapid onset, yet what controls how fast they develop remains unclear and may differ fundamentally across climate regimes. Here we deliver a global, process-oriented assessment of flash drought onset speed using satellite-derived evaporative stress to characterize land-surface water–energy limitations, and SHAP (Shapley Additive Explanations) to diagnose the dominant hydrometeorological drivers of acceleration. We find that while humid regions experience flash droughts more frequently, events in drylands intensify more rapidly. This contrast reflects differences in energy and water constrains: net radiation plays a greater role in humid regions, whereas surface drying dominates in drylands. Moreover, short-term antecedent moisture recovery followed by rapid drying accelerates onset, with soil moisture depths and timescales exerting region-specific influences. These results reveal climate-dependent mechanisms underlying flash drought intensification and highlight the need for tailored monitoring strategies in diverse hydroclimatic contexts.
How to cite: Gou, Q. and Yuan, H.: Could a Brief Wet Spell Accelerate Drought Onset? Climate-Dependent Mechanisms Behind Onset Speed, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8545, https://doi.org/10.5194/egusphere-egu26-8545, 2026.
Compound Drought and Heat Events (CDHEs) pose a growing threat to agricultural systems under a warming climate. This study evaluates mid-century shifts in CDHE characteristics and cropland exposure across Australia using high-resolution CCAM-ACS simulations for Shared Socioeconomic Pathway 1–2.6 (SSP1-2.6) and Shared Socioeconomic Pathway 3–7.0 (SSP3-7.0). An ensemble of seven bias-corrected regional climate models was used to compute the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI), from which CDHE characteristics were derived. Analyses were performed for the historical (1985–2014) and future (2030–2059) periods, and cropland exposure was quantified through the integration of gridded cropland fractions with CDHE occurrence across the eight Natural Resource Management (NRM) clusters. The findings reveal a nationwide intensification of compound drought–heat stress. CDHE frequency increases by approximately 15–30% under SSP1-2.6, with a sharper 20–60% escalation under SSP3-7.0. The strongest rises occur across the Murray-Basin, Central-Slopes, and East-Coast clusters. Event intensity strengthens by 10–25% in the low-emission future and by 30–50% in the high-emission scenario. Event duration also lengthens across most of Australia, indicating a 5–15% increase, while northern and eastern hotspots experience up to 20–25% longer events. The estimates show systematic rightward shifts across all CDHE metrics, reflecting higher probabilities of more frequent and energetically stronger events. When combined with projected cropland patterns, exposure increases markedly. Historical exposure (≈100–300 km² yr⁻¹) rises to 200–350 km² yr⁻¹ under SSP1-2.6 and up to 250–500 km² yr⁻¹ under SSP3-7.0, with the largest increases across southeastern and southwestern cropping belts. Several NRM clusters begin transitioning toward persistently high-exposure states by mid-century. The attribution analysis shows that most of the mid-century increase in cropland exposure is driven primarily by the climate-change component—far exceeding the contribution of cropland shifts—under both SSP1-2.6 and SSP3-7.0. Overall, the findings highlight a substantial escalation in compound drought–heat risk for Australian agriculture and underline the need for climate-resilient cropping systems and regional adaptation strategies.
How to cite: Rezaiebalf, M. and H.C. Chua, L.: Exploring the Future Cropland Exposure to Compound Drought and Heat Events from High-Resolution CCAM-ACS Simulations over Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-179, https://doi.org/10.5194/egusphere-egu26-179, 2026.
Streamflow droughts, i.e., below-average river discharge for an extended period, pose significant challenges to the regional water-food-energy nexus. While several assessments have so far analyzed space-time trends in streamflow droughts, mostly driven by the delayed arrival of the monsoon or large-scale climate variability, the spatiotemporal trends in compound streamflow drought characteristics, considering sequential and concurrent multiple anomalous weather and climatic stressors, have not been assessed at the continental scale. We analyze streamflow records from over 250 sites worldwide at a centennial scale (1901–2023) and demonstrate that, globally, the overall frequency of streamflow droughts has increased significantly over time, with a rate of rise for uncompounded streamflow droughts is approximately 5 events/year over the analysis period. While the compound streamflow drought frequency has shown a relatively weaker significant increase in frequency (~0.5 events/year) than the uncompounded streamflow droughts, spatially a significant spatial clustering of compound drought is observed across the arid (44%), followed by sub-humid (23%) climate regimes. Meanwhile, approximately over half (~56%) of catchments show at least a two-fold increase in streamflow drought deficit volume (severity) when drought onset is compounded by hot and dry compounding events, described by lower-than-normal precipitation deficit followed by higher-than-normal potential evapotranspiration within ±2 months of drought initiation, compared to uncompounded streamflow droughts. A higher likelihood of compound droughts is observed during the boreal summer season, spanning from June to August across the Northern Hemisphere, while an intense drought likelihood is apparent during the austral summer season, varying from December to February in the Southern Hemisphere. The results of this study underscore the importance of considering multi-hazard investigation of hydrological droughts for improving drought preparedness within the short- to long-term planning horizons.
How to cite: Raut, A. and Ganguli, P.: Observed Streamflow Record Shows Streamflow Drought Onset during Hot–Dry Compounding Increases the Likelihood of Intense Droughts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-207, https://doi.org/10.5194/egusphere-egu26-207, 2026.
The Amazon basin is increasingly threatened by severe droughts, traditionally attributed to precipitation deficits. However, the amplifying role of rising evaporative demand, represented by potential evapotranspiration (PET), is not well quantified. Using a counterfactual decomposition framework based on Standardized Precipitation-Evapotranspiration Index (SPEI) from 1979 to 2024, this study quantifies the contributions of precipitation and PET to drought severity and coverage to better understand the evolving drought mechanisms in the region.
Our analysis of the record-breaking 2024 drought reveals that while precipitation deficit was the primary contributor, surging evaporative demand acted as a strong amplifier, nearly doubling the event's severity compared to a precipitation-only scenario. Consequently, 76% of the basin experienced exceptional drought conditions (or D4 drought, SPEI below 2nd percentile) during the peak of the 2024 event. We identify a fundamental regime shift in the 21st century where the contribution of PET to drought area has systematically increased. The basin is transitioning from a precipitation-dominated regime to a "hot drought" paradigm, where compound events, characterized by moderate rainfall deficits exacerbated by high atmospheric thirst, now drive the majority of exceptional drought coverage. Deconstructing the drivers of this rising evaporative demand shows that it can be attributed almost equally to both regional warming and increased surface shortwave radiation from reduced cloud cover. Overall, this study indicates that global warming and regional radiative feedbacks are making the Amazon basin more susceptible to rapid drying even without extreme rainfall deficits.
How to cite: Zhuang, Y. and Tang, X.: The Growing Role of Evaporative Demand in Driving Extreme Droughts in the Amazon Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3030, https://doi.org/10.5194/egusphere-egu26-3030, 2026.
Sustainable water resources management constitutes a critical challenge in Mediterranean regions, where water availability is limited. In addition, over these areas climate change projections point to an increasement of frequency and recurrency of extreme events, such as drought, which further intensification of scarcity conditions. Historically, these regions have addressed their climate variability through regulation and storage infrastructures. However, the resulting increase in water availability caused by this infrastructure has promoted the development of highly water-dependent socioeconomic systems (e.g. irrigated agriculture, energy production or tourism), thereby increasing their vulnerability to these extreme events such as drought.
Drought is a complex phenomenon composed of multiple stages interconnected through a propagation cascade: meteorological drought, driven by precipitation deficits; agricultural drought, linked to soil moisture and vegetation water requirements; hydrological drought, reflected in reduced streamflow and reservoir storage; and socioeconomic drought, which emerges when water shortages impact human activities and services. In this sense, a precipitation deficit does not immediately translate into a reduction in soil moisture or a decrease in streamflow, as drought propagation is modulated by propagation thresholds and lags times. The magnitude and duration of these lags are controlled by multiple factors such as soil characteristics, land uses, and reservoir operation. In Mediterranean mid- mountains catchment this complexity increases due to the variability in precipitation patterns, a complex soil-land interaction and the ephemeral character of the streams.
In this context, this work analyses the thresholds that trigger the concatenation of droughts and the lag times along the drought propagation cascade in medium-sized Mediterranean mountain basins, with the aim of improving the anticipation and management of water scarcity episodes. The analysis focuses on the northern area of Córdoba regions (southern Spain), where recent drought episodes had had a significant impact on water resources availability, exposing structural vulnerabilities in the supplying system. 80,000 citizens were without running water at home for more than a year.
A distributed, physically based hydrological model is applied to generate catchment-averaged precipitation, streamflow, and soil moisture for the period 1960-2024. Drought propagation thresholds and lags are quantified through a comparative analysis of standardized drought indices, including the Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSFI), and Soil Moisture Anomaly (SMA), combined with time-series techniques such as cross-correlation and autocorrelation analyses. Finally, the potential benefits of incorporating these identified lags into operational water management will be evaluated, highlighting their value for strengthening early warning systems and water resources planning.
Acknowledgements: This study has been funded by the call “Grants to develop innovative solutions to address drought, within the framework of the PLAnd Drought Andalusia. 2023 Call” through the project PLSQ-00172-F – “Service for the early detection of alert states in water management under scarcity conditions” (SEGA)
How to cite: Herrera, F., Santos, L., Andreu, A., Contreras, E., Gómez-Beas, R., Aguilar, C., Polo, M. J., and Pimentel, R.: Defining thresholds and lags times in drought propagation cascade: a study case in the northern Córdoba (southern Spain), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10892, https://doi.org/10.5194/egusphere-egu26-10892, 2026.
Drought propagation is a non‑linear, multiscale process linking meteorological, soil, and hydrological drought through temporal conditioning and spatial coherence within river basins. Understanding these interactions is essential for drought early warning and regional risk assessment, yet their quantification remains challenging, particularly in regions with strong land–atmosphere feedbacks such as Poland. In this study, we apply two complementary methodological frameworks—Causality Chain Model (CCM) and Network Correlation Analysis (NCA)—to characterize the spatio‑temporal evolution of drought over Poland using monthly SPI, SPEI and SRI datasets for 1980–2020.
The CCM identifies robust cause–effect transitions along the sequence meteorological → soil → hydrological drought, with propagation delays (DPT) ranging from 1 to 12 months, depending on regional hydroclimatic conditions and indicator aggregation scales. Metrics including DPCs (Drought Propagation Counts) and DPCs_proc reveal that while not every meteorological drought propagates further, a substantial proportion does, forming statistically significant synergistic sequences. The DIP (Drought Intensity Propagation) index indicates regions where drought intensity amplifies during propagation (DIP > 1), highlighting the role of soil moisture depletion and catchment storage deficits in reinforcing hydrological drought development across Poland.
The NCA provides a spatially explicit perspective, identifying propagation hubs, coherent clusters, and regions with strong cross‑catchment connectivity. High values of Degree Centrality and Closeness Centrality reveal locations acting as spatial initiators or transmission nodes of drought signals. The CDC (Closeness to Drought Center) metric further delineates centres of synchronized drought evolution, enabling recognition of areas with elevated susceptibility to persistent hydrological stress.
By integrating CCM and NCA, this study offers a comprehensive multiscale characterization of drought propagation over Poland, capturing both temporal causality and spatial coherence. The combined framework provides actionable indicators supporting regional drought risk assessment, hydrological regionalization, and climate adaptation planning, improving the capacity to anticipate how drought conditions evolve under future climate variability.
How to cite: Tokarczyk, T. and Szalinska, W.: Spatio‑temporal synergies in compound drought propagation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19434, https://doi.org/10.5194/egusphere-egu26-19434, 2026.
The prolonged 2023–2026 drought has exposed critical vulnerabilities in the hydro-infrastructural systems of Southern Italy, where drinking water supply depends on a coupled network of snow-fed reservoirs, spring systems, and karst aquifers. This contribution proposes a multi-hazard analytical framework that links snow drought, hydrogeological deficit, and storage infrastructure reliability, expanding beyond the well-documented Camastra Dam case to include the critical behavior of the Val D’Agri, Fossa Cupa and other regional aquifers. Using drought-propagation models, remote sensed data, and high-resolution in-situ datasets, we analyze how snow-pack deficits propagate through surface reservoirs and karst systems with different lag times, generating asynchronous yet convergent supply failures. The 2019 Camastra Dam’s forced drawdown and 2024 crisis—driven by NTC18 seismic design-reliability regulations, outlet malfunction, and reduced inflow—served as a “early system-scale indicator,” anticipating district-level shortages later confirmed by declining groundwater heads and reduced spring discharge across the southern Apennines.
Building on these observations, this study proposes a unified reliability framework that integrates: (1) climate drivers (snow drought, reduced recharge), (2) hydrogeological pathways (karst storage, delayed meltwater propagation), (3) infrastructure performance and regulatory constraints (NTD14, NTC18 and related design requirements, outlet failures, storage restrictions), and (4) operational risk for drinking-water districts in Basilicata, Puglia, Campania. Preliminary results reveal the emergence of a system-wide tipping condition in which both reservoirs and karst springs lose buffering capacity—an unprecedented scenario for Southern Italy.
How to cite: Faggella, M., D'Ecclesiis, G., and Barbosa, A. R.: Multi-Hazard Reliability of Reservoirs, Aquifers, and Springs in Basilicata under Snow Drought and NTC18 Regulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20843, https://doi.org/10.5194/egusphere-egu26-20843, 2026.
Identifying homogeneous rainfall regions is a fundamental step in regional hydrological analysis. Traditional regionalization approaches often rely on predefined physiographic boundaries or purely statistical clustering, which may inadequately capture complex spatial dependencies in hydroclimatic variables. In regions such as Kerala, India, characterized by complex topography, strong monsoon gradients, and frequent flood events, conventional regionalization methods fail to adequately capture spatial dependence in rainfall variability. This study proposes a Graph Neural Network- based framework for delineating homogeneous rainfall regions to support regional flood frequency analysis and flood risk studies.
Daily gridded rainfall data from the India Meteorological Department (IMD) over Kerala were represented as nodes in a graph, with edges defined by geographical proximity. A two-layer Graph Convolutional Network was trained to learn local rainfall similarity and spatial connectivity. The resulting node embeddings were clustered using the K-means algorithm to identify homogeneous rainfall regions.
Despite using only rainfall information and spatial adjacency, the derived zones closely align with elevation gradients, effectively separating coastal, midland, and western ghats regimes and capturing sharp orographic transitions. This demonstrates that GNN node embeddings can implicitly learn physically meaningful rainfall-topography relationships, providing a robust basis for rainfall regionalization and flood-related hydrocimatic assessments.
How to cite: Saji, N. and Kavirajan, R.: Graph Neural Network -based identification of homogeneous rainfall regions over Kerala, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16024, https://doi.org/10.5194/egusphere-egu26-16024, 2026.
Europe is experiencing an increasing risk of long-term drought as a result of anthropogenic climate warming, declining snowpack in mountainous regions, and changes in large-scale precipitation regimes. These processes contribute to the intensification of climate extremes and pose growing challenges for water-resource management, ecosystem resilience, and socio-economic stability. While CMIP6 climate projections are widely used to assess future drought risk across Europe, the potential implications of solar geoengineering for the spatio-temporal behaviour of long-term droughts remain unexplored.
This study presents a conceptual, scenario-based framework to examine long-term meteorological drought dynamics over Europe using CESM2 simulations from both CMIP6 shared socioeconomic pathways (SSP2–4.5 and SSP5–8.5) and GeoMIP6 solar geoengineering experiments (G1–G4 and pi-control). Drought conditions are evaluated using SPI-12, and drought characteristics—severity, duration, and intensity—are quantified using run theory to enable consistent comparison across contrasting climate-forcing pathways.
The proposed framework facilitates a structured multi-scenario assessment of drought responses under conventional greenhouse-gas-driven warming and idealized solar-radiation-modification scenarios, while maintaining scientific neutrality regarding the feasibility, deployment, or governance of geoengineering interventions. By jointly examining these pathways, the analysis aims to identify potential shifts in drought persistence, intensification, and large-scale spatial expression, key elements governing the spatio-temporal organization of long-term droughts and compound drought risk.
Overall, this work contributes to a more comprehensive assessment of long-term drought risk in Europe by explicitly linking climate extremes to both traditional climate forcings and hypothetical geoengineering perturbations. The framework is transferable and provides a robust basis for drought risk assessment, supporting adaptation planning and long-term drought governance under deep uncertainty associated with future climate trajectories.
Keywords: Climate extremes; Drought; SPI-12; Solar geoengineering; Europe.
How to cite: Kumar, V., Brocca, L., and Gaona, J.: Climate Forcings, Solar Geoengineering, and Long-term Drought Dynamics over Europe , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10744, https://doi.org/10.5194/egusphere-egu26-10744, 2026.
Posters on site: Mon, 4 May, 10:45–12:30 | Hall A
In this work, meteorological drought in Poland has been characterized considering the Standardized Precipitation Index (SPI) evaluated at different timescales (3, 6, 12 and 24 months) from the ERA5-Land monthly dataset provided by ECMWF under the framework of Copernicus Climate Change Service Programme. With this aim, trend detection employed Sen’s slope estimator and Mann-Kendall test, and drought characteristics (e.g., quantity, duration, severity, and intensity) were derived using the run theory applied to the SPI values calculated in 4,084 grid points. As a result of the trend analysis, the short-term SPI (3-month) exhibits pronounced spatial and temporal variability, with trends that are generally weak and less spatially coherent. As the aggregation scale increases to 6 months, trend patterns become more structured, reflecting seasonal to interannual precipitation variability. The long-term SPI scales (12- and 24-month) show more consistent and spatially persistent trends, indicating clearer long-term wetting tendencies across the country.
As regards the drought characteristics, considering the average values, the number of drought events decreases markedly as the SPI time scale increases, with the highest number of events observed for the 3-month SPI and the lowest for the 24-month SPI. In contrast, the average drought duration increases with increasing SPI time scale. Droughts identified using longer accumulation periods persist for longer durations, with the 24-month SPI showing the highest median and variability in duration. A similar increasing trend is observed for the average drought severity, where longer SPI scales are associated with more severe drought events, reflecting the cumulative nature of long-term precipitation deficits. The average drought intensity shows a slightly decreasing trend as the SPI time scale increases. Although intensity remains relatively stable across time scales, droughts identified at shorter SPI periods tend to be marginally more intense than those detected at longer accumulation periods.
With respect to the drought characteristics, considering the extreme values, drought frequency remains relatively stable across the SPI time scales, with only minor variations in median values. In contrast, maximum drought duration exhibits a clear increasing trend with increasing SPI time scales. The short-term SPI identifies extreme droughts with relatively limited durations, whereas the 24-month SPI substantially captures longer extreme drought events, with both higher median values and greater variability, reflecting the ability of longer SPI time scales to represent prolonged drought persistence. A similar pattern is observed for maximum drought severity, which increases markedly with SPI accumulation period. Extreme droughts identified at longer time scales accumulate larger precipitation deficits, resulting in significantly higher severity values, particularly for the 24-month SPI, also showing the widest range of variability. Conversely, maximum drought intensity shows a decreasing trend as the SPI time scale increases. Higher intensity values are associated with shorter SPI periods, while longer accumulation periods tend to smooth short-term variability, leading to less intense but more persistent extreme drought events. Finally, the spatial distribution of the drought characteristics in Poland allows us to identify the areas that could also face water stress conditions in the future, thus requiring drought monitoring and adequate adaptation strategies.
How to cite: Romio, A., Gaudio, R., Walega, A., Walega, A., De Marco, A., Chiaravalloti, F., and Caloiero, T.: Meteorological drought variability in Poland by means of the ERA5-Land dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5159, https://doi.org/10.5194/egusphere-egu26-5159, 2026.
In the Polish Carpathians, periods of precipitation deficit have been observed, accompanied by an increasing frequency of dry months, particularly during the cold half of the year. Despite this, research addressing the spatial and temporal variability of meteorological droughts and the main mechanisms governing their occurrence in Central Europe remains limited.
The objective of this study is to analyze the spatial and temporal variability of droughts, expressed using the Standardized Precipitation Index (SPI), in the heterogeneous area of the Polish Carpathians and highland Region in East-Central part of Europe based on long term precipitation data. Additionally, for the first time, drought characteristics assessed using the SPI were discussed in relation to synoptic situation types (circulation types).
The study region is the Upper Vistula Basin located in the southern and south-eastern part of Poland. The area of this region is approximately 51,000 km2, i.e. a quarter of the entire Vistula basin. In this work monthly precipitation form 56 rainfall station were analysed from 1961 to 2022 years. Meteorological droughts were identified using Standardized Precipitation Index (SPI) calculated over 3-, 6-, 9-, and 12-month accumulation periods. For the 3-month SPI, the main climatic mechanisms responsible for extreme drought events were identified based on a circulation type calendar. Trends in extreme drought occurrence were detected using the Mann-Kendall test.
Statistically significant trends of SPI were observed on 52.7% of all analyzed stations, and in most cases, a positive trend was observed, indicating an increase in water resources in the Upper Vistula Basin. Such significant trends occurred more frequently at stations located in the western part of the analyzed region. Long-term droughts, represented by the 12-month SPI, were recorded at all stations, although not in all years. Short-term droughts, defined using the 3-month SPI, occurred most frequently during winter, while droughts based on the 6- and 9-month SPI were most common in winter and spring, and those represented by the 12-month SPI primarily occurred in winter and autumn.
The most intensive drought episode occurred in 1984, when drought conditions based on the 6-month SPI affected 98% of the analyzed region, and those based on the 9- and 12-month SPI covered approximately 90% of the entire region. Drought occurrence followed a clear seasonal pattern, with a dominant 10-year periodicity observed for all analyzed SPI timescales. In addition, Fourier analysis revealed a 2-year periodicity for the 3-, 6-, and 9-month SPI, and a 31-year periodicity for the 12-month SPI.
The results provide insights into the typical climatic conditions in Poland, characterized by strong precipitation seasonality. The study highlighted that short-term extreme droughts, represented by the 3-month SPI, are often caused by anticyclonic situations with high-pressure wedges Ka (anticyclonic wedge or ridge of high pressure) and Wa (west anticyclonic situation), as observed in 52.3% of cases. Overall, the findings provide valuable insight into the spatial and temporal variability of both short- and long-term extreme droughts in Central Europe, with particular relevance for the agricultural sector, which dominates the northern part of the analyzed region, where drought frequency is highest.
How to cite: Wałęga, A., Cebulska, M., Wałęga, A., Ziernicka-Wojtaszek, A., Młocek, W., and Caloiero, T.: Spatio - Temporal Variability of Meteorological Droughts in Central Europe Considering Circulations Type, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3875, https://doi.org/10.5194/egusphere-egu26-3875, 2026.
Understanding the spatiotemporal variability of drought is critical for assessing its impacts on water resources and terrestrial ecosystems. Despite extensive drought research, basin-scale drought characteristics and their long-term changes have rarely been explored globally within a three-dimensional identification framework, particularly from a multi-index perspective. In this study, drought events across 59 major global river basins during 1979–2020 were identified using the standardized precipitation evapotranspiration index (SPEI) combined with a three-dimensional clustering approach. To assess the robustness of drought characterization, results derived from SPEI were further compared with those based on the standardized precipitation index (SPI). Overall, most river basins did not exhibit statistically significant long-term trends in drought occurrence. Spatially, drought events detected by both indices were largely concentrated along river corridors, highlighting the close coupling between drought evolution and basin hydrological structure. Basin size strongly modulates drought behavior: larger basins tend to experience longer-lasting and more severe droughts, whereas smaller basins are characterized by more frequent but weaker events. Temporal analysis revealed pronounced periodicity in drought variability, especially in small- and medium-sized basins, while drought-affected area and severity consistently increased with event duration. Comparative analysis between SPEI and SPI revealed broadly consistent spatial patterns but notable regional differences in drought frequency and severity. In low- and mid-latitude regions, including South America, Central Africa, and parts of Asia, SPEI identified more extensive and persistent drought events than SPI, suggesting a stronger sensitivity of drought characteristics to temperature-related effects. In contrast, high-latitude and temperate basins generally showed similar drought responses across the two indices. Relationships among drought area, severity, intensity, and duration exhibited comparable behaviors for both indices, with drought area and severity tending to increase over time, while drought intensity showed a gradual decline in most basins. Furthermore, atmospheric circulation was found to exert a stronger influence on drought variability in coastal basins than in inland regions. These findings provide new insights into basin-scale drought dynamics and their controlling mechanisms under a three-dimensional perspective.
How to cite: Feng, X. and Wu, X.: Three-Dimensional Assessment of Drought in Global River Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4104, https://doi.org/10.5194/egusphere-egu26-4104, 2026.
Foreland and lowland regions in Europe are highly dependent on water stemming from the alpine water tower. Due to an increasing number of droughts, expected to exacerbate under progressing climate change, these regions experience severe economic and environmental impact. This affects e.g. hydropower production, ecosystem health, agriculture and drinking water supply. To take foreward-looking adaptation measures, there is the need to understand future climatic drought patterns and their impact on streamflow.
In order to evaluate and analyze past and future alpine droughts, a regional single-model initial-condition large ensemble (SMILE) with 50 members from 1991 - 2100, bias-corrected via MBCn and statistically downscaled, is used. This ensemble approach ensures the quantification of natural climate system variability, while improving the robustness of the results in future climate projections. To enhance interpretability and comparability, a global warming level approach is used. For each warming level (1.5°, 2°, 3° and 4°C), combined drought-heat events in summer (June to August) and snow-drought events in winter (December to February) are identified. To assess the impact of these drought events on discharge, a hydrological large ensemble for selected alpine catchments for the period 1991 – 2100 is created with the Water Balance Simulation Model (WaSiM), using the processed data of the SMILE as forcing.
The results of this study indicate, how future climate will change the water balance in selected alpine catchments based on the return frequency of severe drought events in summer and winter and their impact on runoff. Particularly, this study examines the cascading effects of winter snow-droughts on subsequent summer water availability, revealing how reduced snowpack accumulation under warmer conditions intensifies summer compound drought-heat events. By analyzing different global warming levels, the results provide scenario-independent insights, that are relevant for any emission pathways reaching these specific warming levels. This approach allows for direct comparison with policy goals, such as those specified in the Paris Agreement, and provide stakeholders with a concrete framework for assessing climate risks, regardless of the considered time frame.
How to cite: Pentenrieder, M. and Ludwig, R.: Alpine droughts under climate change: Assessing the relationship and impacts of combined summer drought-heat and winter drought events using a hydrometeorological model ensemble, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5333, https://doi.org/10.5194/egusphere-egu26-5333, 2026.
Abstract
Recent decades have witnessed intensifying drought across the Arabian Peninsula, yet scientists poorly understand whether precipitation deficits or increased potential evapotranspiration (PET) drive this intensification. This study employs the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Evaporation Differential Index (SPEDI) at 3, 6 and 12-month timescales to assess drought across the Arabian Peninsula from 1975 to 2024 using ERA5 Land reanalysis data validated against observed meteorological stations. We isolated each variable’s contribution through diagnostic scenarios, holding either PET or precipitation at climatological means while varying the other. Validation results demonstrated exceptional ERA5 Land performance for temperature variables (mean R = 0.99, NSE > 0.89) and adequate performance for precipitation (mean R = 0.72, NSE = 0.48). Temporal analysis revealed intensifying multi-year droughts with drought-affected areas increasing by 20 to 133 percent between the first (1975–1999) and second (2000–2024) periods of the study across all zones. The Frequency Innovative Trend Analysis (F-ITA) confirms a systematic decline in wet anomalies and increases in drought frequency with the southwestern zone experiencing the most pronounced shift, where mild drought rose from 14.6 percent to 37.6 percent for SPEI 12. The SPEI scenarios revealed that PET contributes 68 to 77% of drought trend variability across climatic zones, while the contribution of precipitation is only 23 to 32%. In SPEI scenarios, when PET is held constant (PETclm), significant drying trends largely disappear; conversely, drought intensification exceeds observed trends when precipitation is held constant (Prclm), confirming thermodynamic forcing as the primary driver. The findings demonstrate that rising temperatures will determine future drought severity in the Arabian Peninsula, necessitating fundamental shifts in water resource management from precipitation-centric approaches toward strategies explicitly addressing temperature-driven PET.
Keywords: Drought intensification; SPEI; SPEDI; Potential evapotranspiration; ERA5-Land; Climate change; Arabian Peninsula
Acknowledgment
This work was supported by the Korea Environmental Industry & Technology Institute (KEITI) through Water Management Program for Drought, funded by the Korea Ministry of Climate, Energy and Environment(MCEE)(2480000378).
How to cite: Rahman, G. and Kwon, H.-H.: Drought Trends and Variability in the Arabian Peninsula Using SPEI and SPEDI Indices and their Implications for Climate Adaptation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6261, https://doi.org/10.5194/egusphere-egu26-6261, 2026.
Floods constitute one of the most catastrophic natural hazards globally, precipitating extensive socio-economic disruption, infrastructure failure, and loss of life. Despite their severity, traditional flood hazard assessments frequently rely on univariate paradigms that assume independence between pluvial and fluvial drivers. Such approaches often overlook the critical reality of compound events, where synchronized or successive drivers amplify the total magnitude of the hazard. This study addresses this gap by proposing a rigorous copula-based framework for assessing Compound Pluvial–Fluvial Flood (CPFF) risk. The methodology employs a block maxima approach to capture extreme events, which are subsequently paired through a lag-time analysis to identify temporal synchronization between extreme precipitation and peak streamflow. A significant refinement in this framework is the integration of a bankfull discharge threshold; this serves as a physical constraint to filter the block maxima data, ensuring that only hydraulically significant fluvial events are analyzed. The joint probabilistic behavior of these flood pairs is quantified using bivariate copula functions, facilitating the estimation of joint return periods for both conjunction and disjunction scenarios. This study validated the framework in the Tapi River basin, India, where intense monsoon seasonality prevails. The findings show that flood risk varies significantly across the basin; rather, it is a function of monsoon-driven precipitation patterns, antecedent soil moisture conditions, and basin-scale hydrodynamic responses. A key finding reveals a spatial gradient in synchronization: upstream catchments exhibit lower correlation between pluvial and fluvial extremes, whereas the downstream reaches demonstrate high synchronization and significantly elevated CPFF risk. By quantifying these interactions, this study highlights that conventional univariate models substantially underestimate the hazard potential in downstream areas, providing a more robust evidence base for regional flood mitigation and infrastructure design.
Keywords: Compound floods, Copula, Statistical analysis, Joint return period, Flood risk assessment.
How to cite: Singh, A., Shukla, S., and J. Sharma, P.: A Copula-Based Framework for Quantifying Compound Pluvial and Fluvial Flood Risks , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6424, https://doi.org/10.5194/egusphere-egu26-6424, 2026.
Agricultural drought poses a major challenge to food security in India, where crop production is largely dependent on the availability of rainfall and soil moisture. Despite extensive research, most drought assessments in India remain region-specific, limiting a holistic understanding of compound agricultural drought risk at the national scale. This study presents a nationwide, district-level assessment of agricultural drought risk across India by integrating drought hazard, exposure, and vulnerability within a unified framework. The assessment is performed for the period 1966–2014 using long-term hydroclimatic, agricultural, and socioeconomic datasets. Agricultural drought hazard is quantified using a copula-based approach that explicitly captures the concurrence of meteorological and soil moisture drought conditions, thereby characterizing compound drought events. Exposure is estimated using percentile-based normalization (5th and 95th percentiles) of population and agriculture-dependent indicators. Vulnerability is evaluated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), incorporating socioeconomic and infrastructural indicators. The results reveal pronounced spatial and temporal variability in agricultural drought risk across India. An elevated risk was found for the Indo-Gangetic Plain, particularly in Uttar Pradesh and Bihar, during the years 1966, 1979, 2009–2011, and 2012. In contrast, north-western India, including Rajasthan, Punjab, and Haryana, experienced heightened compound drought risk during 1987–1988 and 2001–2003. Central India, encompassing Madhya Pradesh and Maharashtra, also emerged as a major hotspot in 1992, 2001–2002, and 2012, while Bihar and Jharkhand exhibited elevated risk in 1983 and 1992. These evolving regional patterns demonstrate the capability of the proposed framework to monitor the spatial progression of agricultural drought risk across districts over time, in association with changes in drought hazard, exposure, and vulnerability, highlighting the importance of regionally targeted drought risk management and adaptation measures.
How to cite: Venkatesh, K., Sivakumar, B., and Onof, C. J.: Spatio-temporal Analysis of Agricultural Drought Risk across India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7245, https://doi.org/10.5194/egusphere-egu26-7245, 2026.
Accurate rainfall estimation is a fundamental prerequisite for effective hydrological drought monitoring. In fact, precipitation represents the primary input to most drought indicators, and even small systematic biases can significantly affect the identification, timing, and severity of drought events. This is particularly relevant for the indices, such as the Standardized Precipitation Index (SPI), which rely exclusively on precipitation time series and are widely used for operational drought monitoring at multiple temporal scales. In regions such as Italy, characterized by complex topography, coastal–mountain interactions, and an uneven distribution of rain-gauge stations, uncertainties in rainfall estimation can therefore propagate directly into drought assessments, potentially limiting the reliability of decision-support systems. To address these limitations, satellite-based precipitation products have become an essential complement to ground observations, providing spatially continuous coverage and near–real-time data. However, their performance varies considerably depending on retrieval methodology, spatial resolution, and prevailing meteorological conditions, making a comprehensive evaluation necessary before their application to drought monitoring.
The objective of this study is to assess how different satellite precipitation products affect SPI-based drought characterization over Italy. Five widely used satellite precipitation products (CHIRPS, GPM, HSAF, PDIRNOW, and SM2RAIN) were selected to represent a broad range of retrieval approaches, including infrared–station hybrid techniques, passive microwave integration, geostationary multi-sensor blending, neural-network–based infrared methods, and soil-moisture inversion algorithms. Their diverse temporal and spatial resolutions make them suitable for both scientific analyses and operational monitoring frameworks.
The SPI data derived from each satellite product were compared. The analysis highlights substantial differences in SPI magnitude, frequency, and duration depending on the input precipitation dataset, emphasizing the sensitivity of drought assessment to rainfall estimation errors. Results indicate that no single satellite product consistently outperforms the others across all metrics and temporal aggregations and suggest that integrating multiple satellite products or adopting hybrid approaches can improve the reliability of SPI-based drought monitoring over complex Mediterranean environments, enhancing early warning capabilities and supporting more informed water-resources management.
This work was funded by the Next Generation EU—Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277)—project Tech4You—Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions; neither the Ministry for University and Research nor the European Commission can be considered responsible for them.
How to cite: Coscarelli, R., Chiaravalloti, F., and Pellicone, G.: Analysis of drought events in Italy evaluated by means of rainfall remote products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7292, https://doi.org/10.5194/egusphere-egu26-7292, 2026.
Sub-daily rainfall extremes that drive rapid urban flooding are expected to intensify under anthropogenic climate change. Yet, their attribution remains uncertain due to limited observation records, lack of adequate representation of relevant physical processes and coarse spatio-temporal resolution of climate models. For a rapidly-urbanizing highly-populated country such as India with ambitious growth targets, such extremes are critical as urban flooding is often associated with significant loss of lives, environment and socio-economic damage. We assess the contribution of human-induced climate change to sub-daily extreme rainfall and its implications for urban flooding over the high-density heritage city of Ahmedabad. Two Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) ensemble outputs, Hist-Nat (historical natural, a counterfactual driven only by solar and volcanic forcing) and Hist (factual, natural plus anthropogenic forcing) are bias-adjusted and statistically downscaled using ISIMIP3-BASD on six most-recent generation models at 0.25° resolution. Temporal disaggregation of rainfall from daily to hourly scales is carried out using a simple, yet effective k-nearest neighbour (kNN) approach evaluated against observations. Rainfall Intensity-Duration-Frequency (IDF) curves are derived for various return periods relevant to urban flood management. Observations show significant increases in short-duration rainfall intensities for Ahmedabad, ranging from 2.9% to 49.1% across different return periods with rarer events showing larger intensifications.
However, model simulations aren’t consistent with each other in terms of nature of change in rainfall extremes, resulting in equivocal attribution conclusions. While the multi-model mean suggests anthropogenic forcing has intensified short-duration rainfall extremes (1-13 hours) by 5-10% and reduced long-duration events (14-24 hours) by approximately 15%, individual models show divergent responses. These findings highlight limitations of current global climate models in attributing sub-daily rainfall extremes to climate change in the Indian monsoon region where fidelity of such models have been questioned by earlier regional studies on seasonal means. It is interesting to note, however, that based on observations alone, short duration high intensity rainfall extremes are found to be rising in this city, concurrent with expansion of built-up areas, thereby increasing exposure of urban population and environment to the risk of flooding.
How to cite: Mondal, A. and Gulzar, A.: Role of climate change in urban flood-relevant sub-daily rainfall extremes in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10399, https://doi.org/10.5194/egusphere-egu26-10399, 2026.
Global food production is concentrated in a limited number of highly productive breadbasket regions, making global supply increasingly sensitive to climate shocks and demographic change. Using multi-model CMIP6 projections under three Shared Socioeconomic Pathways, this study assesses future changes in the frequency, duration, and severity of compound drought–heat extremes across global wheat breadbaskets and evaluates their simultaneous occurrence across regions. Results indicate substantial intensification of compound climate stress, with a growing likelihood of concurrent high-impact years affecting multiple breadbaskets, particularly under higher-emission scenarios. These climate risks increasingly intersect with demographic transitions, including aging agricultural workforces and rising dependence of food-importing regions on external supplies, which may constrain adaptive capacity and amplify supply vulnerabilities.
How to cite: Sabut, A. and Mishra, A.: Co-evolution of Compound Climate Extremes Across Global Breadbasket Regions , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14199, https://doi.org/10.5194/egusphere-egu26-14199, 2026.
Terrestrial Water Storage (TWS) drought is a major hydrological hazard with severe impacts on water resources security, crop yield, natural ecosystem production, and socioeconomic stability. Precipitation has long been assumed as the major driver in the development of TWS drought, but recent work highlights evapotranspiration (ET) as a key additional driver—yet its specific mechanisms and relative importance remain underexplored. In this study, we first derive ET using the terrestrial water budget from observational data (2003–2019) and then propose a diagram to unravel the role of ET anomalies and precipitation minus runoff (PR) anomalies in driving TWS drought intensification and recovery across diverse climate regions. Our results show an asymmetric role of ET in TWS drought dynamics: positive ET anomalies (ET+, ET exceeding climatology) frequently drain TWS and intensify TWS drought, while negative ET anomalies (ET-) preserve TWS and promote TWS recovery. Regional patterns of ET and PR in driving TWS drought development differ markedly. Drought intensification is driven mainly by the combination of ET+ and PR- in arid regions, while ET+ often offsets PR+ to lead drought intensification in humid regions. Drought recovery is predominantly driven by PR+ in hyper-humid and humid regions but is more commonly dominated by ET- than by PR+ in arid and semi-arid regions. These findings provide new perspectives into the complex, indispensable role of ET in TWS drought development, highlighting the need to incorporate ET processes into improved drought monitoring, prediction, and management frameworks.
How to cite: Liu, R. and Liu, L.: The Indispensable Role of Evapotranspiration in Driving Terrestrial Water Storage Drought Development, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16856, https://doi.org/10.5194/egusphere-egu26-16856, 2026.
Extreme and persistent rainfall plays a critical role in shaping hydroclimatic risks, yet its long-term behavior at the watershed scale remains poorly characterized. This study examines observed changes in areal precipitation across standardized watersheds over the Korean Peninsula, with an emphasis on rainfall persistence, spatial variability, and extreme events. Daily areal precipitation for the period 1973–2024 was calculated from surface observations of the Korea Meteorological Administration using the Thiessen polygon method to improve spatial representativeness.
The analysis identifies clear multi-decadal shifts in precipitation characteristics. Mean annual areal precipitation increased from the 1970s to the early 2000s, reaching approximately 1,370 mm, before showing a slight decrease in recent years. Despite this moderation in mean values, heavy rainfall events exceeding 50.0 mm day⁻¹ exert a dominant influence on annual precipitation totals, with a strong correlation (Pearson r ≈ 0.95). This indicates that year-to-year variability in water availability is largely controlled by a small number of intense rainfall events rather than by changes in average conditions.
Spatial variability of heavy rainfall has increased notably since the early 2000s, as reflected by a rising coefficient of variation among watersheds. Rainfall persistence analysis further shows that moderate rainfall events (≥10.0 mm day⁻¹) commonly persist over consecutive days, with a mean Rainfall Persistence Index of approximately 1.4, highlighting the importance of sustained wet periods for hydrological processes. Frequency analysis based on the Generalized Extreme Value distribution reveals that, in the post-2013 period, estimated 100-year return levels of daily areal precipitation exceed 800 mm in several watersheds, indicating an increased potential for extreme rainfall hazards.
Overall, the results demonstrate that hydroclimatic change over the Korean Peninsula is expressed more strongly through shifts in rainfall persistence, spatial heterogeneity, and extremes than through changes in mean precipitation. The findings support the use of watershed-scale areal precipitation analyses for improved assessment of climate-related hydrological risks.
How to cite: Ham, H.: Observed Changes in Extreme and Persistent Areal Precipitation over Standardized Watersheds in the Korean Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23250, https://doi.org/10.5194/egusphere-egu26-23250, 2026.
Global climate change is altering hydrological extremes worldwide, yet how droughts and floods evolve jointly and what drives their changes in India remains poorly understood. Using the Catchment Attributes and Meteorology for Large-sample Studies – India (CAMELS-IND) dataset, we analyze long-term changes in hydrological drought and flood extremes across 55 minimally regulated Indian catchments (reservoir index < 0.25) during 1980–2017. Trends in annual minimum 7-day flows (Q7min) and annual maximum daily flows (Qmax) are quantified using robust non-parametric methods, and their concurrent behavior is classified using a quadrant framework. Results reveal widespread drying, with 38% of catchments exhibiting simultaneous declines in low and high flows, while only 13% show opposing trends indicative of divergence between extremes; remaining basins exhibit weak or mixed changes. Median trend magnitudes reach −3.3% per decade for drought flows and −4.5% per decade for flood flows. Fixed-effects panel regression shows that climate variability dominate streamflow changes, while terrestrial water storage anomalies significantly influence both drought and flood extremes, highlighting groundwater’s critical buffering role. In contrast, land-cover change shows weak or negligible effects. These findings provide the first India-scale, observation-based assessment of joint hydrological extremes and underscore emerging risks to long-term water security.
How to cite: Tiwary, S. and Mondal, A.: Hydrological drought and flood extremes across Indian river basins using CAMELS-IND, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23015, https://doi.org/10.5194/egusphere-egu26-23015, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot A
EGU26-8393 | Posters virtual | VPS9
The role of High Temperature-Low Precipitation conditions in shaping heatwaves and droughtsWed, 06 May, 14:39–14:42 (CEST) vPoster spot A
Hydroclimatic extremes have extensive social, economic and ecological impacts, thereby making it highly imperative to develop disaster assessment and mitigation strategies. The frequency of extreme events like heatwaves and droughts is intricately linked with the rising temperature trends and the changing precipitation patterns worldwide. Moreover, lagged responses amongst such extremes can occur across temporal scales due to the existing large-scale climate linkages. However, the association between present-day occurrences of concurrent high temperature and low precipitation days (HTLPs) with the frequency of heatwaves and droughts of a subsequent period is not fully explored. In this global analysis, we estimate the frequency of heatwaves and droughts based on 1-year temporally lagged HTLPs. Our results reveal a significant rising trend in the average number of heatwaves with an increase in the number of HTLPs of the previous year, while no significant trend is observed for droughts. However, a high number of HTLPs (over 100 events) is associated with a slight reduction in the number of heatwaves (5.9 to 5.6) but a pronounced increase in the number of droughts (1.8 to 2.4). During a 10-year validation period, 81% of heatwaves and 85% of droughts globally remain consistent with the HTLP–conditioned behavior inferred from the 34-year training period of the model. Our findings thus demonstrate the applicability and effectiveness of HTLPs in predicting heatwaves and droughts. This study can be used to develop stochastic models to predict heatwaves and droughts with HTLP as a predictor, and hazard-specific probabilistic assessments that can support and improve resource allocation at regional and global scales.
How to cite: Sinha, D. and Rajulapati, C.: The role of High Temperature-Low Precipitation conditions in shaping heatwaves and droughts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8393, https://doi.org/10.5194/egusphere-egu26-8393, 2026.
EGU26-14649 | ECS | Posters virtual | VPS9
Prognosticative De-Volatility Modeling for Empirically Quantifying CESM and ECMWF Space-Time Heterogeneity of Drought Indices Across Colorado and Louisiana Regions of the USAWed, 06 May, 14:42–14:45 (CEST) vPoster spot A
Although drought indices can be evaluated employing linear and non-linear algorithms, most contributions in the literature have not adequately quantified geospatial-temporal volatility, leading to Type II errors. This study addresses these gaps by comparing ten drought indices across the Colorado and Louisiana regions of the United States over 75 years, examining non-linear and spatio-temporal patterns to ensure a robust assessment of drought. High-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) gridded monthly total precipitation data for 75 years (1950-2024) were used to evaluate the drought indices. The spatial clustering of precipitation patterns was quantified using the second-order semi-parametric eigen-decomposition geospatial autocorrelation to geolocate hot and cold spots of precipitation. We employed the Autoregressive Integrated Moving Average (ARIMA) model, coupled with the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, and compared five ARIMA-GARCH variants across nine error distributions to address non-asymptotic conditional volatility and temporal persistence in precipitation. Drought indices were examined across five temporal scales and contrasted with simulated parameters derived from the Community Earth System Model (CESM). The temporal lag relationship between meteorological and agricultural droughts was evaluated using the non-parametric Time-Varying Distance Cross-Correlation Function (TV-DCCF). The findings revealed that the ARIMA-eGARCH(1,1) model with a Student’s t distribution precisely detected the non-asymptotic conditional volatility in the precipitation time series. The Standardized Precipitation Index (SPI), China Z Index (CZI), and Z-Score Index (ZSI) were the most applicable indices for drought monitoring in both regions. TV-DCCF revealed that meteorological droughts significantly influenced agricultural droughts, with a lag of up to four months. CESM-derived drought indices were mainly within the ERA5-Land uncertainty range, except for CZI and aSPI, attributable to CESM’s lower spatial resolution and limited sensitivity to localized extreme events.
Keywords: Standardized Precipitation Index (SPI); Global Moran’s Index; Autoregressive Moving Integrated Average (ARIMA); Generalized Autoregressive Conditional Heteroscedastic Model (GARCH); ERA5-Land; Community Earth System Model (CESM).
How to cite: Choudhari, N., Elshorbany, Y., Jacob, B., and Collins, J.: Prognosticative De-Volatility Modeling for Empirically Quantifying CESM and ECMWF Space-Time Heterogeneity of Drought Indices Across Colorado and Louisiana Regions of the USA, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14649, https://doi.org/10.5194/egusphere-egu26-14649, 2026.
