This session on hydrological extremes seeks to unite the flood and drought research communities to learn from the similarities and differences in their work. The goals are to deepen understanding of the processes governing these hydrological extremes and their interplay, develop robust methods for modelling and analyzing floods and droughts and their transitions, assess the influence of global change on hydro-climatic extremes, and study the socio-economic and environmental impacts of both types of extremes.
We welcome submissions that present insightful flood and/or drought research, including case studies, large-sample studies, statistical hydrology, and analyses of flood or drought non-stationarity under the effects of climate-, land cover-, and other anthropogenic changes. Studies that investigate both extremes, or their interplay, are of particular interest. We especially encourage submissions from early-career researchers.
Orals: Wed, 6 May, 14:00–08:40 | Room C
Rapid onset, swift intensification, and pronounced hydroclimatic stress define flash drought, which emerges from an initial precipitation shortfall combined with persistently elevated air and land surface temperatures. These conditions substantially increase evaporative demand, driving a sharp decline in soil moisture. In contrast to conventional seasonal droughts that develop gradually, flash droughts escalate to peak intensity within two to three weeks and may continue for several weeks (up to 18 pentads). Their abrupt nature poses a serious threat to agricultural productivity, with cascading effects on food security and the national economy, especially under a warm climate. This nature of flash drought imposes significant hydroclimatic stress, and numerous recent studies highlight the urgent need for a deeper understanding of these rapidly developing drought conditions. However, the absence of a consistent and universally accepted definition has hindered efforts to assess and monitor flash droughts effectively. While multiple climatic drivers, including abrupt transitions in monsoon, elevated temperatures, and vapour pressure deficit, contribute to flash drought development, it is unlikely that a single definition can fully capture their complexity. Nevertheless, it is essential to distinguish flash droughts—short-lived, rapid-intensifying events—from conventional droughts, which typically develop gradually and persist over longer timescales. In this study, we propose a new definition of stand-alone flash drought based on rapid declines in soil moisture, independent of conventional drought classification. This approach enables the recognition of flash droughts as distinct events and underscores their unique characteristics. Using pentad-scale soil moisture data across India, we develop a simple yet robust framework to identify historical flash drought events that have contributed to a reduction in crop yield and vegetation cover, posing significant risks to the national economy. This approach enhances drought characterisation in a changing climate and supports more effective monitoring and impact assessment.
How to cite: Kumari, P. and Vinnarasi, R.: Hydroclimatic Stress in India: Methodological Innovation and Agricultural Relevance of Stand-Alone Flash Droughts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-370, https://doi.org/10.5194/egusphere-egu26-370, 2026.
Australia frequently experiences severe and widespread droughts, causing impacts on agriculture, the economy, and human health. The effects of these droughts can extend beyond national boundaries, influencing the global carbon cycle and global food markets owing to Australia’s position as one of the world’s leading grain exporters. Yet we lack comprehensive understanding of how Australian droughts have evolved over the past century. In this study, we analyse the past changes in seasonal-scale meteorological, agricultural, and hydrological droughts – defined using the 15th percentile threshold of precipitation, soil moisture, and runoff, respectively. We complement these traditional metrics with an impact-based drought indicator built from government drought reports using machine learning. We find that although there have been widespread decreases in Australian droughts since the early 20th century, extensive regions have experienced an increase in recent decades. However, these recent changes largely remain within the range of observed variability, suggesting that they are not unprecedented in the context of the historical drought events. The drivers of these drought trends are multi-faceted, and we show that the trends can be driven by both mean and variability changes in the underlying hydrological variable. Additionally, using explainable machine learning techniques, we unpick the key hydrometeorological variables contributing to agricultural and hydrological drought trends. The influence of these variables varies considerably between regions and seasons, with precipitation often shown to be important but rarely the main driver behind observed drought trends. This suggests the need to consider multiple drivers when assessing drought trends.
How to cite: Ukkola, A., Grant, M., Vogel, E., Hobeichi, S., Pitman, A., Borowiak, A., and Fowler, K.: Reversal of Australian drought trends over recent decades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4596, https://doi.org/10.5194/egusphere-egu26-4596, 2026.
Drought frequency is projected to increase in mainland Europe while decreasing in Scandinavia under climate change, placing Denmark in a hydro-climatic transition zone with an uncertain future. This makes Denmark a relevant case for analysing future changes in the meteorological forcing and hydrological response, that can ultimately lead to substantial implications for agriculture and water resource management.
We investigate future changes in drought frequency and intensity in Denmark up to 2100, combining regional climate model simulations (Euro-CORDEX), which were adjusted using observational data to produce Klimaatlas Denmark, and hydrological model outputs from the National Hydrological Model of Denmark. Drought conditions are analysed using standardised indices representing different components of the hydrological cycle, including precipitation (SPI), precipitation-evapotranspiration balance (SPEI), soil moisture (ESSMI), streamflow (SDI) as well as shallow and deep groundwater (SGDI). We compared these indices using a drought multi-threshold method for a consistent comparison across variables.
Results show a clear seasonal signal in meteorological drought, with decreasing frequency in winter and increasing frequency in summer. Accounting for evapotranspiration further amplifies projected summer drought conditions. In contrast, streamflow and groundwater droughts are projected to decrease in frequency towards the end of the century across most of Denmark. However, extreme droughts may still lead to risks of groundwater depletion, due to increased demands for irrigation water abstraction during dry summers.
These findings demonstrate that changes in meteorological drought do not directly translate to streamflow and groundwater drought responses in Northern Europe. Further, they highlight the importance of accounting for drought propagation through multiple compartments of the natural system and the overlying signal of water provisioning when assessing future drought risks.
How to cite: Häberli, R., Christensen, O. B., Thejll, P., Kaas, E., Schneider, R., and Seidenfaden, I. K.: Future changes in frequency and intensity of meteorological and hydrological droughts in Denmark, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5003, https://doi.org/10.5194/egusphere-egu26-5003, 2026.
Groundwater stores a third of all global freshwater and supports water supply, irrigation and ecosystems across the world. As such, groundwater drought can have wide-reaching financial, social and environmental impacts, particularly when drought events are prolonged or multi-year. Although recent work has made significant progress in understanding the drivers and patterns of multi-year meteorological droughts, we do not know how this signal translates into multi-year groundwater drought, where subsurface processes and anthropogenic pumping or abstractions can alter the meteorological signal. This is particularly true at the global-scale, where a major barrier to understanding large-scale groundwater drought dynamics is the difficulty of obtaining consistent and comprehensive groundwater data.
In this research, we use a new global hyper-resolution (30 arc-seconds or ~1 km) groundwater dataset produced by the global groundwater model GLOBGM to investigate the global trends, patterns and drivers of groundwater drought from 1960-2019, with a specific focus on multi-year events. We start by characterizing the relationship between meteorological drought (represented by SPEI-12) and groundwater drought, evaluating how and to what extent the sub-surface plays a role in modulating the meteorological signal. Subsequently, we categorize the global groundwater data based on its relationship with the meteorology to provide a framework for understanding the processes and geo-physical drivers of normal versus multi-year groundwater drought events in each category. We find that 35% of the world has an average groundwater drought duration which is multi-year. In 84% of these locations, the subsurface extends the meteorological drought signal, whilst in the remaining 16% the groundwater appears to be primarily driven by SPEI-12. We found that pooling of meteorological droughts, the presence of abstractions and lag in groundwater response time are the main drivers for multi-year groundwater droughts. Our analysis offers new insights into global-scale drought exposure and can help inform strategies for managing and mitigating future water scarcity risks.
How to cite: Salwey, S. and Wanders, N.: Global patterns, drivers and trends of multi-year groundwater drought, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7542, https://doi.org/10.5194/egusphere-egu26-7542, 2026.
Droughts are among the costliest of natural disasters in Europe resulting, with average reported losses of about 621 million euros per event [1]. Yet seamless drought monitoring, modelling, and forecasting across spatial scales and time remains a major challenge in hydro-meteorological sciences, in particular when information is needed at actionable kilometre-scale resolution and with low latency.
We address this need with the high-resolution European Drought Monitor (EDM, https://www.ufz.de/index.php?en=52233) as part of the European Space Agency funded DTE Hydrology Next project. EDM provides continentwide products at 1 km resolution, daily updates, and an operational latency of about 6 days. EDM builds on the precursor system of the German Drought Monitor [2, 3] and on a series of European-scale mHM demonstrations [4–10].
EDM runs the mesoscale Hydrologic Model (mHM, https://mhm-ufz.org) on a single pan-European domain. Near-real-time meteorological forcing is taken from ERA5-Land and downscaled to 1 km using external drift kriging (EDK), followed by bias correction using the EMO dataset. Operational production is implemented on an ecFlow backend. EDM provides daily gridded states and fluxes and delivers the soil moisture index (SMI) drought indicator as a core product.
We evaluate EDM against independent observations across multiple components of the terrestrial water balance. Median streamflow skill is Kling–Gupta efficiency (KGE) = 0.38 across 1466 GRDC gauges. Modeled evapotranspiration shows very high agreement with gridded FLUXNET products (correlation = 0.99). Further evaluation uses Earth observation-based (EO) datasets: ESA CCI soil moisture (correlation = 0.54) and GRACE/GRACE-FO total water storage (correlation = 0.61). Together, these results demonstrate that EDM provides spatially and temporally consistent drought diagnostics across Europe at high resolution.
Planned developments include (i) upgrading soil moisture physics by replacing the current infiltration-capacity approach with a Richards equation representation [11] to improve volumetric water content realism, (ii) incorporating atmospheric EO products for near-real-time model initialisation, and (iii) exploiting EO constraints for irrigation water-use estimation and reservoir state verification. By providing EO-informed, kilometre-scale drought surveillance, EDM supports the Sendai Framework’s call for improved hazard monitoring and enables timely, locally relevant drought warnings for Europe.
References
[1] https://www.emdat.be
[2] doi: 10.1088/1748-9326/11/7/074002
[3] doi: 10.5194/hess-26-5137-2022
[4] doi: 10.1175/JHM-D-15-0053.1
[5] doi: 10.5194/hess21-4323-2017
[6] doi: 10.1088/1748-9326/aa9e35
[7] doi: 10.1175/JHM-D-18-0040.1
[8] doi: 10.1038/s41558018-0138-5
[9] doi: 10.1175/BAMS-D-17-0274.1
[10] doi: 10.1029/2021EF002394
[11] doi: 10.1029/2024WR039625
How to cite: Shrestha, P. K., Lenz, K., Modiri, E., Kelbling, M., Kholis, A. N., Lüdke, V. S., Najafi, H., and Samaniego, L.: The European Drought Monitor – EO-powered 1-km daily drought monitoring with 6-day latency, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7629, https://doi.org/10.5194/egusphere-egu26-7629, 2026.
Reliable characterisation of soil-moisture drought is critical for water management, yet hydrological models can diverge substantially because of parametric uncertainty [1] even when forced with identical meteorology. This work is conducted within the ESA 4DHydro initiative (https://4dhydro.eu/) and builds on our EO-constrained parameter estimation framework [2]. We assess whether Earth Observation (EO) data reduce this divergence using a four-model ensemble (CLM, JULES, mHM, PCR-GLOBWB) over the Rhine Basin. We compare three parameter estimation strategies: (i) a non-EO baseline using default model configurations, (ii) EO-only calibration using satellite soil moisture (SM) and evapotranspiration (ET), and (iii) a hybrid EO+Q calibration combining EO constraints with streamflow (Q).
The latter ensures both spatial pattern matching of EO-derived SM, ET, and water balance closure. For the major droughts of 2015, 2018, and 2019, EO-only calibration notably reduces inter-model spread and strengthens the detection of extreme dry conditions, improving ensemble agreement by up to ~0.09 in extreme-event cases. Joint SM+ET calibration provides the best trade-off between sensitivity to extremes and ensemble stability across models.
The EO+Q strategy yields the highest temporal skill, including station-scale improvements (e.g., RMSE reductions of ~0.02 and correlation gains of ~0.06 in independent validation), but also exposes larger between-model differences, especially in Alpine headwaters where snow and glacier processes remain challenging. Overall, EO constraints can meaningfully tighten multi-model drought estimates, while also highlighting persistent structural uncertainties that should be communicated in operational drought early-warning systems.
References:
[1] Samaniego, L., Kumar, R. and Attinger, S., 2013. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Journal of Hydrology, 476, pp.253–265.
[2] Modiri, E. et al., 2026. Toward improved soil moisture drought representation through Earth Observation constrained parameter estimation: A multi-model ensemble analysis over the Rhine River basin. In submission to HESSD.
How to cite: Modiri, E., Rakovec, O., Shrestha, P. K., García-García, A., Avila, L., Blackford, K., Cooper, E., Droppers, B., Filippucci, P., Fischer, M., Orság, M., Stradiotti, P., Brocca, L., Clark, D., Dorigo, W., Kollet, S., Peng, J., Wanders, N., and Samaniego, L.: Added Value of Earth Observation Constraints for Multi-Model Drought Detection in the Rhine Basin , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9592, https://doi.org/10.5194/egusphere-egu26-9592, 2026.
With cascading impacts on agriculture and the livelihoods of millions of people, drought is one of the most severe natural hazards affecting water and food security in India. While meteorological droughts occur due to an initial precipitation deficit, their propagation to streamflow depletion depends fundamentally on the catchment characteristics and natural and man-made water storage, which are increasingly modified by human interventions. Over the southern Indian Peninsula, where human influence on river systems is significant, the impact of large-scale human interventions on streamflow regimes and drought mechanisms remains poorly understood. In this study, we investigate how meteorological droughts propagate into hydrological droughts across 14 major river basins in southern India under natural conditions (i.e., without human influence on streamflow) and anthropogenic conditions (i.e., with human influence). Utilizing daily meteorological data (precipitation and temperature) from IMD and streamflow data (observed and model-simulated), we estimate various drought characteristics and propagation dynamics under natural and anthropogenic conditions for each streamflow station during the period 1986 to 2006. Our findings indicate that human interventions to river systems notably delay the onset of droughts and reduce recovery periods, especially during severe events. Moreover, we show a significant difference between how natural and managed hydrological systems respond to meteorological droughts in peninsular river basins. Overall, our results highlight the role of human activities in modulating hydrological drought characteristics and emphasize the importance of considering human activities in evaluating drought risk and monitoring.
How to cite: Gupta, U., Aadhar, S., and Sengar, P.: Influence of intensive human activities on the propagation of meteorological to hydrological drought in the Southern Indian Peninsula , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20922, https://doi.org/10.5194/egusphere-egu26-20922, 2026.
Hydrological droughts are complex spatio-temporal phenomena whose impacts propagate through river networks, affecting water availability, ecosystems, and water management systems (Van Loon, 2015). Understanding how drought events develop and propagate across space remains challenging, particularly when comparing river basins with contrasting hydroclimatic conditions, sizes, and buffering capacities (Wu et al., 2022). Despite the increasing availability of long discharge records, comparative and event-based analyses of hydrological drought propagation are still limited by the lack of scalable and reproducible methodological approaches (Van Huijgevoort et al., 2012).
In this study, we present an event-based framework for the detection, characterization, and propagation analysis of hydrological droughts, designed to enable consistent inter-basin comparisons. Hydrological drought events are identified using threshold-based methods applied to daily discharge series, from which key metrics describing event duration, severity, deficit and lag are derived. Drought propagation is analysed through a network-aware approach that quantifies temporal relationships between upstream and downstream gauging stations along the river network.
The framework has been tested on daily discharge records from the Ebro and Segura River Basins (Spain), representing markedly different basin sizes and hydroclimatic regimes. Results reveal clear contrasts in drought behaviour between both systems. In the Ebro Basin, drought events are associated with large absolute water deficits due to the high-flow regime of the basin, but display comparatively lower relative severity and a weak coupling between event duration and deficit when normalized by local flow conditions, consistent with strong system buffering and spatial heterogeneity. In contrast, the Segura Basin exhibits more recurrent drought events per station, smaller absolute deficits but substantially higher relative severity, longer persistence, stronger coupling between duration and deficit, and a high spatial coherence across the river network, reflecting limited buffering capacity under semi-arid conditions.
These results demonstrate the ability of the proposed framework to capture basin-specific drought regimes and propagation dynamics in a consistent manner. The event-based analysis provides new insights into the controls of hydrological drought persistence and severity and offers a robust basis for comparative drought studies, large-scale impact assessments, and future integration into climate impact analyses and basin-scale hydrological modelling frameworks.
Keywords: Comparative basin analysis; Drought propagation; Hydroinformatics; Hydrological drought; River network connectivity; Spatio-temporal analysis
References
[1] Van Huijgevoort, M. H. J., Hazenberg, P., Van Lanen, H. A. J., & Uijlenhoet, R. (2012). A generic method for hydrological drought identification across different climate regions. Hydrology and Earth System Sciences, 16(8), 2437-2451. https://doi.org/10.5194/hess-16-2437-2012
[2] Van Loon, A. F. (2015). Hydrological drought explained. WIREs Water, 2(4), 359-392. https://doi.org/10.1002/wat2.1085
[3] Wu, J., Yao, H., Chen, X., Wang, G., Bai, X., & Zhang, D. (2022). A framework for assessing compound drought events from a drought propagation perspective. Journal of Hydrology, 604, 127228. https://doi.org/10.1016/j.jhydrol.2021.127228
How to cite: Crespillo López, A., Vicente Serrano, S. M., and Gimeno Presa, L.: Event-based analysis of hydrological drought propagation across contrasting river basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1999, https://doi.org/10.5194/egusphere-egu26-1999, 2026.
Hydrological drought is among the most impactful hydro-climatic extremes, with effects that often propagate along river networks and affect extensive downstream regions. Although drought processes differ fundamentally from floods in terms of their underlying mechanisms, both types of extremes exhibit pronounced nonstationarity under the combined influence of climate change and human activities, and both require advanced statistical tools to characterize their evolving risks. In particular, the dependence structures among hydrological extremes at different spatial locations remain insufficiently understood, especially from the perspective of river-network connectivity and propagation processes.
In this study, we focus on the nonstationary dependence of hydrological droughts along upstream–downstream river systems and the associated concurrence risk. Using a series of hydrological gauging stations distributed along tributaries and the main stem of the Yangtze River basin, we develop a drought concurrence analysis framework based on extreme value theory and nonstationary copulas to characterize the spatio-temporal evolution of multi-site drought dependence. The framework first identifies and matches drought events at the event scale across multiple stations, ensuring that copula modelling is built upon genuinely concurrent extreme drought processes. This event-based treatment avoids the potential mixing of asynchronous drought events that may arise when copulas are directly constructed from time series at fixed time steps. Subsequently, extreme drought characteristics, such as drought duration, are modelled using extreme value theory for the marginal distributions, while time-varying parameters are introduced in a nonstationary copula to describe the evolution of inter-site drought dependence.
Compared with existing studies, the proposed framework addresses two key limitations in current copula-based drought concurrence analyses: the insufficient representation of true event concurrence and the lack of explicit modelling of nonstationary dependence structures. This integrated approach enables a direct comparison of drought dependence between adjacent (local-scale) and non-adjacent (long-range) upstream–downstream station pairs, providing a unified, probabilistic, and transferable statistical tool for quantifying multi-site extreme drought concurrence risk and its temporal evolution.
The results reveal pronounced nonstationarity in upstream–downstream drought dependence, with clear strengthening or weakening trends over recent decades. Notably, dependence structures inferred from non-adjacent station pairs differ substantially from those estimated using only adjacent stations. This finding highlights the importance of accounting for multi-station propagation effects in drought analysis, rather than relying solely on local relationships. The evolution of dependence structures further leads to significant changes in concurrent drought risk, with particularly strong implications for downstream regions, where streamflow dynamics integrate hydrological signals from multiple upstream sub-basins.
By explicitly linking drought nonstationarity, spatial dependence, and concurrence risk, this study contributes to a more comprehensive understanding of hydrological extremes at the river-network scale. The proposed framework is flexible and can be extended to other drought definitions, different river basins, and even to the joint analysis of droughts and floods. The findings provide valuable scientific insights for drought risk assessment and adaptive water resources management under a changing climate.
How to cite: Feng, Y., Wu, Z., and Vicente-Serrano, S. M.: A nonstationary copula-based framework for analyzing hydrological drought concurrence and propagation in river networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19571, https://doi.org/10.5194/egusphere-egu26-19571, 2026.
Droughts have severe global impacts on the environment and economy. They affect crop yield and biodiversity, disrupt water transport, and cause shortages of drinking water. To mitigate these impacts, national weather and environmental agencies operate drought monitoring tools, which are primarily based on weather station data. However, these stations are not homogeneously distributed. Alternatively, satellite remote sensing allows for monitoring droughts contiguously over large areas. Precipitation, vegetation condition, evapotranspiration, and soil moisture estimates from space-borne sensors enable drought monitoring at a large scale. Among them, the soil moisture-based drought index is relevant for plant water availability as it helps to detect the moisture deficit even before the vegetation responds to droughts. Individual satellite sensors have a limited lifespan, and the data from each sensor is usually available for shorter periods of time. Such data are not sufficient to monitor drought conditions, which are long-term phenomena. However, the data can be merged into a long-term record to monitor the drought conditions. In this study, we developed a drought index, the soil moisture anomaly standardized index (SMASI), by merging the standardized soil moisture anomaly from various microwave satellite sensors. SMASI uses soil moisture from sensors that are used to develop the European Space Agency-Climate Change Initiative (ESA-CCI) soil moisture product. The SMASI dataset enables monitoring drought at a global scale with its record spanning more than 35 years. SMASI shows a good agreement with other well-known drought indices such as the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI).
How to cite: Luintel, N., Formanek, M., Bueechi, E., Kovács, D., Preimesberger, W., Moldenhauer, C., and Dorigo, W.: Soil Moisture Anomaly Standardized Index (SMASI): A multi-sensor drought index from soil moisture anomalies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22288, https://doi.org/10.5194/egusphere-egu26-22288, 2026.
A wide variety of processes controls characteristics of water extremes: river floods, droughts, episodes of detrimental streamwater quality. Understanding generation processes of these events may assist in uncovering their emergence and support the interpretation of their changes. Here I show how objective event identification and transferable causative classification frameworks are able to overcome the limitations of locally tailored approaches and detect functional changes in extremes over large spatial domains and long temporal scales.
To pave the way towards more efficient adaptation measures for extremes we need to understand intricate links between their hazard and impact components better. Here I demonstrate how different generation processes of extremes are interlinked with the adaptation efficiency, previous societal experiences and awareness uncovering how they might shape socio-economic impacts. The examples show how bridging across domains can help to improve our preparedness and anticipate future impacts.
How to cite: Tarasova, L.: Water extremes under change: from processes to impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13436, https://doi.org/10.5194/egusphere-egu26-13436, 2026.
Understanding spatial variability in extreme river discharge is crucial for accurately assessing flood risk in extensive, monsoon-dominated river basins. This study highlights the characteristics of extreme discharge and its correlation with flood events along the Brahmaputra River. Extreme hydrological events were identified through a threshold-based methodology, with the 99th percentile of daily discharge established as a benchmark for high-impact flood conditions. To ensure the independence of events, exceedances were declustered using a three-day separation window, retaining only peak discharge values from individual events. The results indicate a pronounced downstream amplification in the magnitude of extreme discharges, characterized by increasing event peaks from upper to lower Assam. This phenomenon reflects the cumulative integration of hydrological processes across the basin, underscoring the influence of basin-wide hydro-climatic factors. Seasonal analysis reveals that extreme discharge events are predominantly concentrated during the monsoon season, underscoring the critical role of monsoon rainfall and the upstream catchment's response in the genesis of flood events. Furthermore, Flood return periods, computed using declustered peak discharges, yield reach-specific flood estimates, indicating significantly higher 50-year return levels in the downstream sections. A comparative analysis of extreme event characteristics between pre- and post-2000 periods reveals an increase in both mean and maximum flood magnitudes in recent years, suggesting potential non-stationarity in the flood regime. The frequency of smaller flood return periods has increased in recent times, primarily due to shifting precipitation patterns within the basin. Overall, this study demonstrates the longitudinal coherence in the behaviour of extreme discharge along the Brahmaputra River, characterized by the downstream amplification of flood magnitude and the persistence of basin-scale drivers influencing extreme event frequency. These findings underscore the importance of conducting spatially distributed assessments of extreme flows for effective flood risk management in extensive Himalayan river systems under changing hydroclimatic conditions.
How to cite: Paswan, A. K., Tiwari, V. M., and Phukan, M. K.: Non-stationarity and spatial heterogeneity of extreme discharge and flood return levels in the Brahmaputra Valley, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17525, https://doi.org/10.5194/egusphere-egu26-17525, 2026.
Flood risks are escalating globally as climate change intensifies land–atmosphere interactions, resulting in large-scale flooding. Yet, existing assessments remain constrained by site-based and stationary assumptions that obscure how extreme floods propagate across space and evolve over time. Addressing this gap, we present the first continent-scale analysis of changing spatio-temporal dependence in Australian flood extremes. Towards this, we develop a comprehensive suite of 3,937 spatial and spatio-temporal max-stable process (MSP) models, integrated with large-scale climate modes and physiographic controls. Leveraging annual maximum floods from 325 Hydrologic Reference Stations and a 30-year moving-window framework (1973–2002), we quantify evolving spatio-temporal dependencies in floods and benchmark MSP performance against non-stationary GEV models to assess the added value of jointly modelling spatial dependence and temporal non-stationarity. Results reveal fundamentally different spatial and temporal behaviours of frequent and rare floods. Frequent floods (with a 2-year return period) weaken across much of southern Australia but intensify in the north, reflecting the dominance of local hydrological and topographic controls. Rare floods (with return periods of 25–100 years) exhibit strong spatial heterogeneity and widespread increases along the east coast, southeast, and tropical north, driven primarily by ENSO, IOD, and SAM, which emerge as the strongest modulators of temporal variability. Physiographic gradients, particularly catchment area, elevation, and slope, govern spatial dependence across the continent. A striking north–south divergence in the evolution of flood coherence is uncovered: southern Australia exhibits increasing synchronisation of floods, whereas northern regions show growing spatial fragmentation. Critically, spatio-temporal MSPs capture these dynamic shifts in flood clustering—features that NSGEV models cannot detect—resulting in substantial reductions in uncertainty in rare-flood quantiles, particularly in data-sparse regions. By integrating local catchment attributes and large-scale climate variability into spatial extremes theory, we provide a unified modelling framework that uncovers how Australia’s flood hazard landscape is being structurally reorganised under climate change, offering a new foundation for continent-scale risk assessment, infrastructure planning, and climate-resilient adaptation.
How to cite: Posa, P. C. L., Wasko, C., Wu, W., and Bhowmik, R. D.: Continental-Scale Dynamics of Flood Extremes: A Unified Spatio-Temporal Modelling Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-448, https://doi.org/10.5194/egusphere-egu26-448, 2026.
Floods arise from the interaction between near-event precipitation and antecedent soil moisture, which regulates how efficiently precipitation is converted into runoff. Studies often classify floods by generating mechanisms using catchment-level statistics and treat soil moisture implicitly as a bulk proxy, obscuring its direct regulatory role and the influence of specific soil layers on individual flood events. This research utilizes explainable AI tools to quantify the event-scale roles of precipitation and multi-layer soil moisture in flood generation globally. We trained a catchment-shared LightGBM model on the Caravan–GRDC dataset to predict daily streamflow percentiles from 1,385 snow-free catchments. To isolate immediate forcing from antecedent state, we utilized lagged inputs: t−1 for precipitation and t−2 for soil moisture at four depths (0–7, 7–28, 28–100, and 100–289 cm). For 38,317 annual-maximum flood predictions, we applied SHAP to decompose each prediction into predictor contributions and classified events as either precipitation-dominant or soil-moisture-dominant based on the largest absolute SHAP value. Results show that soil moisture is the predominant global flood driver; however, precipitation dominance becomes more frequent toward the highest streamflow percentiles, indicating that the largest peaks often require intense forcing to overcome storage constraints. The two regimes exhibit distinct dynamics: Soil-moisture-dominant floods evolve slowly, with longer rising and recession limbs, and are regulated by shallow subsurface moisture (7–100 cm). They typically occur in larger, flatter, and lower catchments with shallow water tables. Precipitation-dominant floods are flashier, with sharp rising limbs, show stronger sensitivity to the surface moisture (0–7 cm), and are more prevalent in smaller, steeper, high-relief catchments, with deeper water tables. The peak timing predictions reflect the challenge of capturing short-fuse storm dynamics relative to slowly evolving storage states, with well-timed soil-moisture-dominant peaks compared to precipitation-dominant peaks, which exhibited timing delays. This framework provides a scalable, event-based quantification highlighting the catchment control, with soil moisture (especially within the upper meter) acting as an active regulator of runoff generation. The dominance classification supports process-aware forecasting and climate-change adaptation by tracking shifts in flood-generation regimes and indicating whether predictability depends more on storm forcing or antecedent catchment state.
How to cite: Kimchi, Y. and Morin, E.: Event-Scale Drivers of Flood Generation: Large-Sample Assessment of the Roles of Soil Moisture and Precipitation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3289, https://doi.org/10.5194/egusphere-egu26-3289, 2026.
Estimating extreme flood events with a return period of 1000 years or more is particularly challenging in ungauged catchments. Traditional methods often rely on statistical extrapolation of peak discharges or regionalized design values, both of which are subject to considerable uncertainty. Our study examines whether continuous hydrological simulations could offer a useful second opinion to these traditional methods. The continuous simulations are based on a model chain that starts with a stochastic weather generator, which produces long synthetic time series of precipitation and temperature. These time series then serve as input to a hydrological model. As there are no direct streamflow observations for ungauged catchments, the model parameters must be regionalized to realistically configure the hydrological model. Initial tests, in which gauged catchments were treated as ungauged, were promising, suggesting that continuous simulation using a stochastic weather generation combined with a hydrological model could provide a robust basis for estimating extreme floods in regions with limited data.
How to cite: Staudinger, M., Kauzlaric, M., Kritidou, E., and Viviroli, D.: Where can I get this darn number? Estimating 1000-year return periods in ungauged catchment areas using continuous simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3771, https://doi.org/10.5194/egusphere-egu26-3771, 2026.
Flood hazards in large river basins are shaped by multiple interacting factors, including how long a flood lasts, how high the peak flow becomes, and how much water passes through the river system. Traditional approaches that focus only on the flood peak often miss important aspects of flood behaviour and can underestimate risks, especially for long, slowly building floods or short, intense flash events. This study provides a basin-wide assessment of flood characteristics in the Narmada River Basin, India, using 50 years of daily streamflow data from 13 monitoring stations. Instead of analysing peak flows alone, we consider three dimensions of flood behaviour—peak discharge, volume, and duration—and combine them into two complementary metrics. The Short-Duration Flood Index (SDFI) highlights quick, intense floods typically driven by burst rainfall and rapid runoff, while the Long-Duration Flood Index (LDFI) represents slower, persistent floods that build up over days as catchments saturate. These indices allow floods to be compared across the entire basin despite large differences in catchment size and hydrologic setting. The analysis reveals clear spatial contrasts in flood behaviour. Upstream mountainous and plateau regions experience more sustained, long-duration floods, reflecting greater storage and slower runoff processes. Mid-basin tributaries show pronounced flashiness and frequent short-duration floods driven by intense rainfall and limited buffering capacity. Downstream areas receive the highest overall flows as water converges from upstream, but exhibit mixed characteristics depending on event type and rainfall distribution. Importantly, the index-based approach identifies severe flood events that traditional peak-only assessments tend to overlook. By capturing both flash floods and slow-building high-volume floods within a single framework, the method provides a more complete picture of basin-wide flood hazard. Although demonstrated in the Narmada Basin, the approach is applicable to other major river systems and can support flood mitigation planning, early-warning design, and water-resource management—especially as climate change continues to alter rainfall patterns and flood regimes.
Keywords: Multivariate flood analysis; Flood indices; Flood frequency analysis; Hydrological extremes; Narmada River Basin
How to cite: Begum M H, N., Swarnkar, S., and Singh, A. D.: A Multidimensional View of Flood Regimes in the Narmada Basin, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9836, https://doi.org/10.5194/egusphere-egu26-9836, 2026.
Flood events can be highly localized in both time and space. Many of the most severe events observed in recent years have occurred in small catchments, where high flow velocities and the rapid development of floods substantially limit the effectiveness of monitoring and hydrometeorological forecasting. When reconstructing the characteristics of such events, together with the analysis of catchment response times, attention is commonly given to soil infiltration and retention processes. In engineering applications, the runoff coefficient is often used to describe the proportion of rainfall that becomes runoff during an event.
For a given watershed, the runoff coefficient is not a fixed parameter; it varies considerably with event intensity and the physical and hydrological characteristics of the catchment. This study proposes a practical methodology for estimating a representative catchment runoff coefficient where high-resolution rainfall and streamflow time series are lacking. Leveraging more than 1,000 historical events from 60 small to medium-sized catchments in diverse climatic regions of Italy, sourced from national Hydrological Yearbooks, we reconstruct annual runoff coefficients from historical hydrological extremes and compare two estimation methods: an event-based and a frequency-based approach.
A key element of the event-based method is the development of a procedure to associate discharge maxima with their corresponding rainfall maxima in time, which was necessary because, in many cases, the dates of occurrence of extremes (day/month) were not recorded. On the other hand, a frequency-based pairing of rainfall and discharge extremes was shown to provide runoff coefficient estimates that are comparably robust to those obtained with the event-based approach.
Further investigation of case studies characterized by unusually low (below 0.1) or high (above 1) runoff coefficients highlighted several sources of data inconsistency that can compromise estimate reliability. The most common issues include spatial mismatches between rain gauge locations and catchment boundaries, transcription errors in historical datasets, and additional runoff contributions, such as snowmelt in Alpine regions. Recognizing and accounting for these factors allowed for a more consistent and reliable estimation of runoff coefficients.
How to cite: Bogoni, P., Evangelista, G., Ganora, D., and Claps, P.: Reconstructing representative runoff coefficients in small basins using limited historical data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11385, https://doi.org/10.5194/egusphere-egu26-11385, 2026.
Traditionally, hazard assessments have relied on the concept of a T-value (or return period) to define the average frequency of extreme events, which is linked to an exceedance probability. However, despite significant advancements in continuous hazard modeling, the most common method for hazard definition still relies on pre-determined probabilities of exceeding a specific event. As a result, current models typically provide hazard maps for specific return periods, often resulting in limited overlap among the considered return periods.
To address this limitation, we introduce a multi-model expected return period framework for extreme events, which estimates the average frequency of climate hazards by integrating multiple open datasets. This approach offers a novel methodological perspective on return period estimation, providing a statistically robust and user-friendly tool to address model heterogeneity. We demonstrate the framework's applicability by utilizing open and accessible web data on flood hazards, specifically three spatial datasets detailing global inland fluvial flood maps: the World Resources Institute's Aqueduct Floods Project maps (Ward et al., 2020), the European Joint Research Center's (JRC) maps (Dottori et al., 2016), and the maps produced by the CIMA Research Foundation and the United Nations Environment Programme (Rudari et al., 2015).
We introduce here a multi-model expected return period value, TMM, determined by an average function based on the number of active models, i.e., the number of models that provide a valid output for a given cell within their active spatial domain. This concept shifts the perspective on extreme event frequency from evaluating hazard as event-, model-, and return-period-specific to a more integrated hazard estimation approach.
Although demonstrated using flood hazard data, the framework can be adapted to any other spatially-explicit extreme event characterized by return periods, such as drought, cold spells, and wind storms. By leveraging the differences among existing datasets, our mathematical framework adds value to open science efforts, introducing a tool to exploit high-quality, open data from distinct modeling designs. This can particularly benefit socio-economically vulnerable communities. Our work showcases the potential of heterogeneous, open data sources to improve climate knowledge and provides a robust foundation for future research in hazard modeling and climate risk assessment.
How to cite: Sciarra, C., Ridolfi, L., and Laio, F.: A Multi-Model Approach to Return Period Estimates: The Example of Floods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11405, https://doi.org/10.5194/egusphere-egu26-11405, 2026.
Abstract
Abrupt transitions between drought and flood—known as Drought–Flood Abrupt Alternation (DFAA)—constitute an emerging class of compound hydrological extremes with significant implications for risk management. This study introduces a systematic framework for detecting DFAA events across 15 UKBN2 (UK Benchmark Network version 2) catchments (1970–2015) using daily SPEI (Standardized Precipitation–Evapotranspiration Index) and SRI (Standardized Runoff Index) time series. We evaluated 27 detection configurations, varying anomaly thresholds, minimum drought durations, and transition windows, to assess sensitivity and classification robustness.
Threshold selection exerted the greatest influence on event frequency, followed by transition-window length, while drought duration played a comparatively minor role. A baseline configuration (threshold ±0.7; drought ≥14 days; flood ≥1 day; transition ≤14 days) delivered the most hydrologically realistic and spatially coherent results. Under these criteria, SRI consistently identified more DFAA events than SPEI, reflecting rapid runoff responses driven by catchment storage and antecedent wetness. Spatial analysis revealed a pronounced west–east gradient, with higher alternation frequency in wetter, low-permeability upland catchments, while seasonal patterns indicated drought-to-flood transitions predominating during recovery from dry spells.
These findings underscore the critical role of index choice, storage dynamics, and transition timing in shaping DFAA behaviour. The proposed framework provides a reproducible basis for monitoring compound drought–flood risks and delivers essential evidence to support future modelling, operational early warning systems, and climate adaptation strategies.
How to cite: Al-Yousuf, S., Han, S., and Hannah, D.: Assessing the sensitivity of detection criteria for Drought-Flood Abrupt Alternation events in the UK, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4982, https://doi.org/10.5194/egusphere-egu26-4982, 2026.
Hydrological extremes display long-term trends and natural oscillations in response to climatic and socio-economic factors. Knowing how and why these extremes are shifting is crucial for increasing water resilience in Europe. This work analyses trends in floods and droughts in European catchments with an upstream area below 100 km2 between 1951 and 2020. Using hight-resolution climatic and socioeconomic data, the OS LISFLOOD hydrological model and non-stationary extreme value analysis, we disentangle the effects of four drivers on flood and drought magnitudes: dynamics in climate – encompassing climate variability and climate change – land use changes, water demand changes and reservoir construction. We map combined floods and droughts changes into four trajectories: wetting, drying, accelerating, decelerating. The trajectories and their links to different drivers are aggregated at different spatial levels, revealing patterns from local to regional scales in changes of flood and drought magnitudes. We find that on average, flood magnitude rose by 1.5% and drought magnitude fell by 1.3% across Europe since the 1950s, with multidecadal variations. Climate dynamics lead to heterogenous patterns, with an overall wetting of north-western Europe, and a drying in the Mediterranean region. Diverse land use changes (e.g., urbanization, reforestation) have generally increased flood and drought hazard (intensification), while water demand primarily intensified droughts (drying). Reservoirs, conversely, have smoothed extremes and decelerated the hydrological cycle. Our work delivers insights into the intricate connections between climate, society, and water at a refined resolution in European river basins, enabling the development of effective strategies for enhancing resilience to extreme water events under global changes.
How to cite: Tilloy, A., Paprotny, D., Mentaschi, L., Grimaldi, S., Gomez-Aragon, D., and Feyen, L.: Climatic and socioeconomic drivers of changing flood and drought magnitudes in Europe from 1951 to 2020, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6934, https://doi.org/10.5194/egusphere-egu26-6934, 2026.
Theory suggests that a non-stationary, warming climate will intensify the global hydrologic cycle. Yet theoretical expectations of intensification are often at odds with observational records. Understanding how potential acceleration affects the transition between hydrological extremes remains a critical knowledge gap relevant to the session’s goal to better understand the interplay between hydro-climatic states. We investigate the changing pace of the water cycle through the lens of observed lag times between atmospheric drivers and terrestrial drought.
Focusing on the conterminous United States from 1950 to 2020, we employ a multi-metric framework to quantify changes. We begin by analyzing soil water residence time and Water Cycle Intensity (WCI) using data from ERA5-Land, in situ observations and gaged streamflow records. Here, a decrease in residence time indicates a faster turnover, or "flashiness," which fundamentally alters how extremes propagate through the landscape. To link these physical rates to societal impacts, we subsequently analyze drought propagation—specifically the lag time between meteorological drought (Standardized Precipitation Index) and agricultural drought (Standardized Soil Moisture Index). Preliminary hypotheses suggest that as the water cycle accelerates, the buffer capacity of the land surface diminishes, leading to faster propagation of precipitation deficits into soil moisture and runoff deficits. By quantifying these changing lag times, this research provides a new, observationally-driven assessment of how the ‘rate’ of drought is evolving, seeking to provide useful insights for early warning systems and the management of non-stationary transitions between wet and dry extremes.
How to cite: Livneh, B., Sabin, M., Bjarke, N., and Duncan, B.: From precipitation to impact: Understanding the impacts climate non-stationarity through on pace of drought, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7370, https://doi.org/10.5194/egusphere-egu26-7370, 2026.
Drought–flood abrupt transitions are a manifestation of hydroclimatic volatility and can lead to hydrologic extremes that are influenced by the amplification of wet-dry anomalies through land-surface dynamics and watershed storage. In addition to meteorological forcing, land cover changes can modify infiltration–runoff partitioning, evapotranspiration feedback, and soil-moisture persistence, thereby reshaping where transition hotspots emerge and how abruptly they evolve. This study quantifies drought–flood transition regimes in the semi-arid Ebro basin and evaluates how land cover changes modulate the propagation of hydroclimatic variability into hydrologic regime transitions.
The Precipitation–Evaporation Anomaly Index (PEAI) is computed from HYPER-P 1km precipitation and GLEAM evapotranspiration, providing a high-resolution description of hydroclimatic deficit–surplus anomalies over 2016–2022. PEAI is integrated with surface soil-moisture anomaly dynamics derived from Sentinel-1 radar to compute high-frequency ΔSM anomalies. These ΔSM anomaly sequences provide the basis for deriving soil-moisture memory (SMM) at fine spatial and temporal scales, with persistence, decay, and instability quantified. Drought–flood transitions are delineated by persistent PEAI anomaly reversals and retained only when accompanied by a coherent ΔSM response, after which events are mapped to delineate hotspots and summarized using metrics of frequency, duration, and abruptness. Land cover change is derived from the Copernicus datasets, which provide annual land-cover maps from 2016 to 2022, and is used to stratify SMM properties and transition metrics to compare transition behavior under similar PEAI variability across areas with different land-cover patterns.
Results show pronounced dry–wet alternations and spatially heterogeneous soil-moisture memory, with short persistence and elevated instability in recurrent transition zones. These SMM signatures sharpen the delineation of hydroclimatic volatility hotspots and improve the spatial identification of rapid drought–flood abrupt transition events. Transition frequency and abruptness are not explained by PEAI intensity alone; instead, they vary systematically with land-cover patterns, revealing distinct transition regimes across cropland-dominated areas, natural vegetation, and expanding built-up surfaces. Overall, integrating PEAI derived from HYPER-P precipitation and GLEAM evapotranspiration with radar-based ΔSM and SMM provides a physically consistent, high-resolution framework to explain how hydroclimatic volatility propagates into hydrologic extremes through drought–flood abrupt transitions shaped by land cover change in a semi-arid Mediterranean basin.
How to cite: Serbouti, I. and Brocca, L.: Hotspots of drought–flood abrupt transitions from hydroclimatic volatility to hydrologic extremes under land cover change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9199, https://doi.org/10.5194/egusphere-egu26-9199, 2026.
Our study analyses streamflow characteristics across the European domain, focusing on changes in the frequency, magnitude, and duration of low and high streamflow events and their interactions. We use the percentile of the Standardized Runoff Index distribution (SRI-P) as a metric to characterize these hydrological events (dry using SRI-P < 0.2 and wet SRI-P > 0.8). The monthly gridded discharge time series are obtained from the multiscale Hydrological Model (mHM), which was forced by meteorologic data from the ISIMIP3b archive, which has been bias-corrected and downscaled using the latest E-OBS observational dataset. We first perform a historical evaluation (1960-2024) to evaluate the model's ability to capture observed streamflow characteristics against observation-based E-OBS meteorological characteristics. The projections are then analyzed according to different Global Warming Levels (GWLs) to identify non-linear responses in streamflow extremes to warming levels. Increasing global warming from 1.5°C to 3°C (with respect to the 1980-2010 GCM specific baseline) essentially intensify European streamflow toward severe and more extended drought conditions. While Northern Europe is projected strong high-flow events, these extremes lose their strength in the south. Instead, the focus shifts to low-flow extremes, which are growing more severe and frequent. This leaves the Mediterranean in a state of pronounced drying, while Central Europe sits in the middle, highlighting its vulnerability to hydrological risk as wet extremes weaken and droughts intensify, elevating pressure from both ends.
How to cite: Rakovec, O., Kettaren, R., Yadav, D., Trnka, M., Hanel, M., and Kumar, R.: Contrasting projected changes in European streamflow extremes across global warming levels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10796, https://doi.org/10.5194/egusphere-egu26-10796, 2026.
OS LISFLOOD is an open-source, spatially distributed, physically-based hydrological model. Notably, it is used in the operational set-up of the Copernicus Emergency Management Service (CEMS) to generate flood forecasts for the European and Global Flood Awareness Systems (EFAS & GloFAS) and drought indicators for the European and Global Drought Observatories (EDO & GDO). Being part of an operational set-up, OS LISFLOOD and its European and global model domain set-ups benefit from regular upgrades, with the release of EFAS version 6 (1 arcmin spatial resolution, 6 hours temporal resolution) and GloFAS version 5 (3 arcmin spatial resolution, daily temporal resolution) planned in 2026.
In a multi-purpose framework, developments included in EFAS v6 and GloFAS v5 aim to improve the representation of all key hydrological states and fluxes, with specific attention to high and low flows, soil moisture, snow cover, total runoff, and total water storage. For example, improved representation of physical processes (e.g. a diffusive river routing and a revised reservoir routine) enables more accurate simulation of river flow dynamics; updated model inputs (such as meteorological forcings and soil properties) and revised model routines (model state initialization, snow melt, and water losses to the deep groundwater) support more realistic representation of snow cover, soil moisture and total water storage dynamics. Moreover, model parameter calibration used a novel objective function, the Joint Divergence Kling–Gupta Efficiency (JDKGE), designed to optimize performance on both high and low flows.
This contribution presents the quantitative evaluation of EFAS v6 and GloFAS v5. OS LISFLOOD calibration utilized 2,318 in-situ discharge time series for Europe and 5,230 globally, with an increase of 22% and 162%, respectively, over previous versions. Median modified Kling Gupta Efficiency (‘KGE) exceeds 0.7 for both systems, representing an improvement of +0.08 and +0.21 for the European and global domains, respectively. Examples of high and low flow simulations for different climate zones and socio-economic landscapes allow to uncover the outcomes of modelling choices, explain challenges, and share open questions. European and global set-ups comprehensive evaluation also entails comparisons with relevant hydrological variables for water resilience such as soil moisture and total water storage.
EFAS and GloFAS hydrological reanalysis datasets are available from the Copernicus Early Warning Data Store. In compliance with FAIR principles, OS LISFLOOD source code (with v5 incorporating all recent model improvements) is freely accessible alongside its pre- and post-processing tools, input maps, and calibrated parameter maps. By providing open access to these datasets and modeling tools, we invite the wider community to benefit from the recent developments, collaborate in model evaluation, and contribute to the ongoing evolution of the system.
How to cite: Grimaldi, S., Salamon, P., Russo, C., Mazzetti, C., Prudhomme, C., and Mastrantonas, N. and the team of co-authors: A multi-purpose modelling framework for improved prediction of hydrological extremes at the European and global scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13772, https://doi.org/10.5194/egusphere-egu26-13772, 2026.
The Bay of Bengal region is critically important both climatically and socioeconomically. Its warm waters influence monsoon patterns, which are essential for sustaining agriculture across India, Bangladesh and Myanmar. However, the region is increasingly experiencing floods and prolonged droughts that disrupt water availability and crop production, threatening the livelihood of millions of people. These not only exacerbate food insecurity but also highlight the vulnerability of densely populated coastal areas to climate variability. Understanding the future dynamics makes the Bay of Bengal a crucial area for climate modelling.
To that purpose, we conducted five high-resolution simulations, each spanning 30 years, using the Weather Research and Forecasting (WRF) model under different Shared Socioeconomic Pathways (SSPs) and time periods. The spatial resolution of the domain is 5 km, with hourly outputs stored. The first simulation was run under present climate conditions (1981–2010). For the future, SSP2-4.5 and SSP5-8.5 scenarios were considered, and two distinct periods were simulated: mid-century (2031–2060) and end-of-century (2071–2100).
The analysis of the Standardized Precipitation Evapotranspiration Index (SPEI) calculated over 6 and 24 months suggest important changes on both short- and long-duration droughts. The probability of short drought occurrence is quadrupled in some areas of central and east India under both scenarios, and tripled in some parts of the Ayeyarwady delta in Myanmar. The severity of the droughts will be exacerbated by the end of the century, particularly over central Myanmar and coastal areas of India in both scenarios. For the long term, SPEI-24 indicates that severe droughts will be found over mainland India under both SSPs. The droughts will intensify also along the western coast of Myanmar and the Ayeyarwady Delta in most of the periods, except for the latest period of SSP5-8.5.
Additionally, changes in flooding are being investigated through flood assessments using coupled HEC-HMS and HEC-RAS modeling over Myanmar. Preliminary findings suggest significant changes in flooding in the Ayeyarwady Delta by the end of the century, with an increase in severity, especially under the SSP5-8.5 scenario. Similarly, flooding is expected to intensify along the Ayeyarwady River from the confluence with the Chindwin River to Magway —covering most of the central dry zone— by the end of the century under SSP-8.5.
Changes in atmospheric dynamics and the monsoon seem to be playing a role in the occurrence of droughts, but further understanding of these connections is needed. Our results highlight emerging patterns of drought and flood risk that warrant closer examination, offering valuable insights for future hydroclimatic assessments and regional planning.
How to cite: González-Rojí, S. J., Messmer, M., and Win, S.: Changing droughts and floods in the Bay of Bengal: Insights from high-resolution climate simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14728, https://doi.org/10.5194/egusphere-egu26-14728, 2026.
Too much and too little water can trigger cascading, multisectoral impacts through floods and droughts, respectively. Although these natural disasters are traditionally analyzed in isolation, their temporal sequencing can produce compounding impacts that exceed the impact of single, independent events. As climate change has already increased the magnitude and frequency of both droughts and floods worldwide, future climatic conditions are also expected to substantially alter the frequency, timing, and characteristics of drought-to-flood transitions.
A few existing studies on these consecutive shifts focus on climate change impacts at a global scale, while the limited number of regional-scale analyses of streamflow drought-to-flood transitions often do not assess how climate change may reshape these transitions at subseasonal and seasonal timescales. Thereby, we shed light on anticipated impacts of climate change on streamflow drought-to-flood transitions across 505 catchments in Brazil by comparing the baseline period (1980-2010) with a near-future period (2015-2040) and a distant-future period (2071-2100) under a medium-emission (SSP2-4.5) and high-emission (SSP5-8.5) scenario derived from 10 bias corrected climate change models. We distinguish between rapid transitions (14 days between extremes) and seasonal transitions (90 days between extremes).
Our results indicate that both rapid and seasonal drought-to-flood transitions in Brazil are projected to more than double by the end of the century under both emission pathways. Notably, approximately 80% of investigated catchments are expected to first experience rapid drought-to-flood transitions in the future, i.e., this share of catchments did not experience such events during the baseline period. This emergence of previously unobserved transitions underscores the urgency of proactive water management to mitigate potential multisectoral impacts of sequential, contrasting extremes. Relative changes substantially increase from the near to distant future under both climate change pathways, with the most pronounced increases occurring in the most populated (e.g., São Paulo in Southeast Brazil) and agricultural-relevant regions (e.g., areas in the Cerrado biome in Mid-West Brazil). Beyond changes in frequency, transition times are also expected to shift. Rapid transitions exhibit increased variability and longer transition times approaching the 14-day threshold, whereas seasonal transitions remain predominantly distributed between 30 and 60 days.
The projected increase in drought-to-flood transition frequency poses significant challenges for water resources management, particularly for systems designed to cope with hydrological extremes independently. Rapid transitions may undermine reservoir operation rules, drought contingency plans, and early warning systems that implicitly assume longer recovery periods between extremes. This is especially critical in densely populated areas, where abrupt shifts from drought to flood can simultaneously strain water storage, allocation, and flood control objectives, amplifying risks to urban water supply and other water ecosystem services. Anticipating these transitions is paramount for adaptive management strategies that incorporate compound-event risk into operational decision-making and adaptation planning.
How to cite: Schwamback, D., Brandão, A., Benso, M., Gescilam Uchôa, J., A. A. Anache, J., Ballarin, A., Gesualdo, G., and Sone, J.: Increased Future Streamflow Drought-to-Flood Transitions across Brazilian Catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15415, https://doi.org/10.5194/egusphere-egu26-15415, 2026.
Anthropogenic pressures, including climate and land-use change, are expected to intensify hydrological extremes, such as floods and droughts, in many regions worldwide. These hydrological changes are likely to have cascading effects on water quality, affecting a range of stakeholders and the ecological health of the freshwater system. However, significant uncertainty exists in projections of future catchment-scale hydrological extremes and the resulting effect on different water quality parameters.
In this study, we assess potential future changes in hydrological extremes and associated water quality responses across five hydrologically diverse catchments in Yorkshire, UK. Climate forcing is derived from 12 UK Climate Projections (UKCP) regional climate models at the highest available spatial resolution of 2.2 km. To account for socio-environmental change, three contrasting land use scenarios are considered, ranging from a sustainable transition (SSP1-2.6) to fossil-fuelled industrialisation (SSP5-8.5). The integrated hydrological and water quality model ‘HYdrological Predictions for the Environment’ (HYPE) is applied within a Generalised Likelihood Uncertainty Estimation (GLUE) framework to predict hydrological droughts and floods and the water quality response for an ensemble of climate and land use projections.
In this talk, we first present how both hydrological droughts and floods are projected to change under future climate and land-use scenarios. We then show how water quality parameters (water temperature, suspended sediments, nitrogen, phosphorus) change during these extreme events. Lastly, we quantify how spatio-temporal uncertainty in climate, land use and HYPE parameterisation propagate through to the simulated flow and water quality parameters.
How to cite: Pink, I., Reaney, S. M., Villamizar Velez, M. L., Boxall, A., and Neill, A.: Water Quality Responses to Floods and Droughts in the Context of Climate and Land Use Change. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19081, https://doi.org/10.5194/egusphere-egu26-19081, 2026.
Detecting the emergence of anthropogenic climate change signals in precipitation is essential for informing adaptation strategies. This study analyses long-term, quality-assured observations from 36 stations across Ireland (1930–2019) to assess trends and emergence in six seasonal precipitation indices. Using a combination of Mann-Kendall trend testing, Theil-Sen slope estimation, and monthly persistence analysis, robust seasonal changes are identified. Emergence is evaluated by regressing local precipitation indices against global mean surface temperature (GMST), with the resulting signal to-noise ratio (SNR) classified as normal, unusual, or unfamiliar relative to early industrial (1850-1900) and modern (1950-1980) baselines. The influence of the North Atlantic Oscillation (NAO) is also assessed using commonality analysis. Results show statistically significant intensification of rainfall extremes, particularly in western Ireland during winter and spring, and in the southeast during summer and autumn. Many stations exhibit significant relationships with GMST, with increases in extreme indices (e.g., Rx5day, SDII) ranging from 12% to 27% per °C of warming, often exceeding thermodynamic expectations. Emergence of unusual climate conditions is already evident at several stations relative to the early industrial baseline, and many are nearing this threshold for the modern baseline. While NAO variability strongly modulates winter precipitation extremes in the west, significant GMST relationships in the SNR analysis indicate that these are still robust climate change signals. Commonality analysis reveals that GMST and NAO jointly explain variability in winter PRCPTOT and Rx5day at western stations, suggesting that natural modes of variability like the NAO may not be independent noise but rather embedded within a warming climate signal, complicating the separation of anthropogenic and natural drivers in attribution studies. Findings also challenge projections of widespread summer drying with warming, instead revealing intensification of short duration extremes in the southeast. As Ireland faces increasingly intense and seasonally variable rainfall extremes, these results highlight the urgency of regionally tailored adaptation strategies grounded in observed climate change signals. These results provide robust observational indications of an intensifying atmospheric water cycle and emerging precipitation extremes over Ireland under anthropogenic warming.
How to cite: Fordham, S.: The Emergence of a Climate Change Signal in Ireland’s Rainfall Extremes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2807, https://doi.org/10.5194/egusphere-egu26-2807, 2026.
Natural hazards, including drought and wildfire, can substantially modify how catchments generate streamflow following precipitation events. Yet, how drought and fire events alter such rainfall-runoff relationships remains poorly understood. Here, we use nonlinear deconvolution techniques to reveal how runoff responds to rainfall in 155 Australian catchments, and how these responses are altered by the 2017-2019 Tinderbox Drought and the 2019-2020 Australian wildfires. Our results show the Tinderbox Drought typically halved runoff response (i.e. the streamflow response to a unit of rainfall reduced by half), and subsequent fire impacts appeared paradoxical because post-fire runoff generation was simultaneously altered by climate conditions, fire severity, and drought legacies. During the drought, almost all catchments exhibited declines in per-unit-rainfall runoff responses, with stronger declines in catchments that experienced larger rainfall reductions and had low soil infiltration capacities. Post-fire, these responses increased by up to threefold in some regions, but these increases appear to have been driven by higher rainfall rather than by fire effects. In contrast, catchments in other regions experienced little overall change in runoff response, because the runoff reductions driven by drought legacy effects competed with simultaneous strong increases in runoff response induced by the wildfires. This highlights that compound natural hazard effects on runoff generation may reshape hydrological processes in diverse and initially unintuitive ways.
How to cite: Liu, S., J. Slater, L., Fowler, K., Veraverbeke, S., and Berghuijs, W.: Compound drought and fire effects on runoff generation in southeastern Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5868, https://doi.org/10.5194/egusphere-egu26-5868, 2026.
Hydrological droughts result from the propagation of meteorological drought through the terrestrial hydrological system. Their temporal dynamics, including onset, development, intermittency, and recovery, show pronounced spatial variability reflecting different climatic and catchment controls. These characteristics are critical for drought risk management because they control the timing, magnitude, and persistence of water deficits, with implications for water supply, agricultural water demand, aquatic ecosystems, and energy production. Despite their importance, these temporal characteristics are seldom examined, and their controlling factors remain insufficiently quantified.
Here, we investigate the climatic and catchment controls on the temporal dynamics of hydrological drought using observations of precipitation, streamflow, and groundwater levels from 132 German catchments for the period 1951 to 2020. Using the Variable Threshold Method, we identify 1,574 hydrological drought events and characterize their onset, development, intermittency, and recovery times. We examine the spatial variability of these characteristics across the study catchments and identify the controls underlying their similarity, including the synchronization between precipitation and evapotranspiration seasonality, catchment storage capacity, and groundwater-surface water interactions (gaining-losing conditions). Within catchments exhibiting similar drought dynamics, we then examine how temporal variations in gaining-losing conditions affect drought onset, development, intermittency, and recovery.
Our findings advance mechanistic understanding of drought dynamics and support improved water resources management under increasing climate variability.
How to cite: Abdelmajid, M., Peña-Guerrero, M. D., Brunner, M. I., de Brito, M. M., and Tarasova, L.: Temporal drought dynamics in Germany: climate and catchment controls, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13165, https://doi.org/10.5194/egusphere-egu26-13165, 2026.
The Indian Summer Monsoon (June–September, JJAS) plays a critical role in regulating water resources, agriculture, and hydroclimatic extremes across India, yet projecting future changes in monsoon variability remains challenging due to the coarse spatial resolution and biases of earth system models (ESMs). In this study, we develop high-resolution (0.25°) projections of monsoon rainfall over India using a statistical downscaling framework that combines weather typing with transfer functions, and we demonstrate how Standardized Precipitation Index (SPI) projections can be used to diagnose future changes in monsoon characteristics. Downscaled simulations from five GCMs are analyzed for a historical period and for two future socioeconomic pathways (SSP2-4.5 and SSP5-8.5). SPI is computed at monthly scale for June, July, August, and September, as well as for the seasonal JJAS total, enabling assessment of both intra-seasonal and seasonal hydroclimatic variability. Evaluation against observations shows that the historical simulations reproduce observed rainfall statistics with high fidelity, capturing both the mean and standard deviation across most regions of India. Furthermore, the downscaled GCMs successfully represent historical extremes, with more than 70% of grid cells capturing observed extreme drought events and over 80% capturing extreme wet events, providing confidence in the robustness of the derived SPI projections. Our next objective is to test the hypothesis that SPI contains discernible signals of key monsoon characteristics, including onset, withdrawal, and intraseasonal variability. If such signals are evident, SPI can serve as a useful diagnostic tool for inferring these features, which are otherwise difficult to predict directly. This framework further enables a range of analyses, including assessment of future projections and evaluation of shifts in the frequency, duration, and intensity of dry and wet spells during the monsoon season under both moderate and high-emission scenarios, thereby revealing changes in intraseasonal variability that may not be captured by seasonal mean rainfall alone.
How to cite: Pathak, A., Kulkarni, S., Salvi, K., Saji, H., Gupta, H., and Tejaswi, B.: Assessment of droughts and extremes over India using CMIP6 simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20241, https://doi.org/10.5194/egusphere-egu26-20241, 2026.
The self-calibrated Palmer Drought Severity Index is a widely used metric for drought monitoring and climate change assessments but suffers from inherent climatic inconsistencies and lacks comprehensive and reliable estimates under changing climate conditions. We developed a monthly multi-model and multi-scenario sc-PDSI dataset (PDSI_CMIP6) for the period 1850–2094, derived from 11 climate model outputs within the Coupled Model Intercomparison Project 6. The traditional two-layer bucket model in PDSI is replaced with direct hydrological outputs from CMIP6 models, ensuring alignment with CMIP6 projections. The PDSI estimates are validated against soil moisture simulations through correlation and regression analysis. Application of the dataset reveals pronounced spatial heterogeneity in long-term drought trends across continents, with limited global-mean change but notable regional intensification under climate change. PDSI_CMIP6 provides uncertainty-constrained quantifications of terrestrial moisture conditions in a changing climate, faithfully reflecting CMIP6-projected hydrological changes.
How to cite: Xiong, J., Yang, Y., and Yang, D.: PDSI_CMIP6: A CMIP6-Consistent Palmer Drought Severity Index Dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2106, https://doi.org/10.5194/egusphere-egu26-2106, 2026.
SM2RAIN-ASCAT is a satellite-based precipitation product that derives rainfall estimates from soil moisture observations using a bottom-up approach. This study evaluates its performance for precipitation estimation and drought monitoring across Morocco by comparing it with in-situ data from 36 ground-based stations covering multiple climate zones. In this context, a range of quantitative and qualitative metrics was used to validate SM2RAIN-ASCAT data against observed precipitation. The Standardized Precipitation Index (SPI) was calculated at 1, 3, 6, and 12-month timescales to assess drought monitoring effectiveness, with performance stratified by climate zone.
Results reveal that correlation coefficients with ground observations increased from 0.45 at the daily time scale to 0.67 at the monthly time scale, with 10-day and monthly aggregations offering the best agreement. The dataset revealed a strong ability to detect rain, attaining monthly Probability of Detection values exceeding 0.75 at 89% of stations. Although the product exhibited a tendency to underestimate intense rainfall events, relative bias remained low at nearly half of the stations, with minimum RMSE values occurring at the monthly scale. Regional performance showed consistent variability, with underestimation in Mediterranean zones and overestimation in arid regions, though drought monitoring capability remained robust.
SPI values for short- to medium-term durations aligned well with ground observations across Morocco's climate zones. Agreement was weak to moderate for 1-month SPI but improved substantially for 3-month and 6-month periods, with correlation coefficients of approximately 0.70 and 0.80, respectively. Long-term drought monitoring using SPI-12 showed particularly strong performance, with excellent agreement at nearly all stations. The product showed superior accuracy in detecting droughts in arid zones against humid zones and wet conditions in hot arid climates compared to wetter climates. These findings suggest that integrating bottom-up SM2RAIN-ASCAT and top-down approaches can enhance precipitation and drought monitoring by addressing the limitations of each method. SM2RAIN-ASCAT is particularly recommended for agricultural drought monitoring and water resource management in arid regions.
Keywords: Standardized Precipitation Index, SM2RAIN-ASCAT, Rainfall, Bottom-up approach, Drought
How to cite: El Goumi, S., Namous, M., Elaloui, A., Krimissa, S., Nait-Taleb, O., Chouidda, H., Elalaouy, N., and Bouras, E.: Assessing SM2RAIN-ASCAT rainfall products for drought monitoring across Morocco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-695, https://doi.org/10.5194/egusphere-egu26-695, 2026.
Drought-wetness abrupt alternation (DWAA) represent a classic form of hydroclimatic whiplash, posing escalating threats to global water and food security. Yet, conventional identification frameworks often simplify these shifts as instantaneous binary events, neglecting the critical, evolving physical dynamics during the transition phase. This study challenges the prevailing “instantaneous-transition” assumption by establishing a process-aware identification framework based on pentad-scale soil moisture dynamics.
Applying this framework to multiple datasets (GLDAS-NOAH-2.1, ERA5, and SMCI) across China from 2000 to 2022, we reveal a fundamental temporal asymmetry in DWAA events. Our results show that drought-to-wetness (DTW) transitions typically occur as rapid, explosive shocks, completing within approximately 10 days with a mean transition rate of +30% per pentad. In contrast, wetness-to-drought (WTD) transitions unfold as prolonged depletions, taking roughly one month to conclude with a significantly slower transition rate of -15% per pentad.
This two-fold timescale difference is driven by distinct physical mechanisms: DTW is dominated by external, high-intensity atmospheric precipitation pulses, whereas WTD is constrained by the internal, nonlinear memory of terrestrial water storage and evapotranspiration-driven depletion. Spatially, these events exhibit segregated hotspots, with DTW clustering in the Huai River Basin and WTD concentrated across the broader Yangtze River Basin and southeastern coast. These findings offer a new physical basis for developing asymmetric, mechanism-based identification systems, shifting the paradigm from simple event cataloging to a dynamic understanding of compound hydroclimatic extremes.
How to cite: Xiao, Z. and Yuan, S.: Unraveling the Temporal Asymmetry of Drought-Wetness Abrupt Alternations: A Process-Aware Framework Based on Pentad Soil Moisture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4742, https://doi.org/10.5194/egusphere-egu26-4742, 2026.
Flash droughts are rapidly developing soil moisture deficits that intensify within weeks and pose growing threats to agriculture, ecology, hydropower, water, and food security. Their behaviour in Himalayan basins remains poorly understood due to complex topographic features, highly heterogeneous soil moisture & land-atmosphere energy fluxes, and the coexistence of snow-fed & rain-fed hydrological regimes. Addressing to this gap and supporting Sustainable Development Goals (SDGs) related to zero hunger (SDG 2), clean water availability (SDG 6), and climate action (SDG 13), this study investigates flash drought characteristics and their elevation dependence in the Indian Indus Basin. Multi-source daily root-zone soil moisture (RZSM) datasets from ERA5, SMAP, FLDAS_CA, and GLDAS (versions 2.1 and 2.2) bilinearly interpolated at 0.1° resolution were evaluated against in-situ measurements from the NGARI network of the International Soil Moisture Networks (ISMN), which identified the ERA5-GLDAS2.1-GLDAS2.2 ensemble as the most reliable RZSM data (R² = 0.7003). The validated RZSM data were converted to 8-day (octad) means, and soil moisture percentiles were computed using an empirical Weibull distribution. The RZSM percentile octads were then used to identify flash droughts in the basin. Flash drought events were detected when percentiles declined from ≥40 to ≤20 within three octads at an intensification rate ≥6.5 percentiles per octad, and persisted below the 20th percentile up to eleven octads. From these events, the mean annual and seasonal onset speeds, durations, severities, and frequencies were quantified. Piecewise linear regression with breakpoints selected using the change in Akaike information criterion (ΔAIC) revealed distinct elevation-dependent regimes. Mean annual flash drought severity, frequency, and duration increased from low to mid-elevation zones (up to ~2000 m) and declined toward higher elevations. Mean annual onset speed was maximum (~17-30 percentile/octad) at low elevations, indicating rapid soil moisture depletion under strong atmospheric demand, whereas higher elevations exhibited slower onset (~6.5-17 percentile/octad), likely due to snowmelt-driven soil moisture replenishment and reduced evaporative demand. Similar elevation dependence regimes of flash drought characteristics were observed seasonally, with maximum frequency (~10-15%) and onset speed (~15-20 percentile/octad) in the monsoon, but the highest duration (~6.5-10 octads) and severity (~100-150) in the post-monsoon. These non-linear elevation responses highlighted the critical role of topography in modulating flash drought evolution in the complex Himalayan basin. This study presents the first elevation-based characterization of flash droughts and demonstrates the value of high-resolution reanalysis-based soil moisture data for enhancing flash drought monitoring in data-scarce mountain basins.
How to cite: Kumar, R., Joshi, N., and Swami, D.: Assessing Nonlinear Responses of Flash Drought Characteristics to Elevation in the Western Himalayan Catchment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16882, https://doi.org/10.5194/egusphere-egu26-16882, 2026.
Compound hydroclimatic extremes, characterized by the simultaneous or sequential occurrence of multiple essential climate variables (ECVs), have significant implications for both water resources management and human health. Climate change is increasingly linked to a rise in the frequency and intensity of extreme temperature and precipitation events worldwide. As such, understanding the interplay between these variables is critical for improving forecasting and formulating effective adaptation strategies. This study investigates the joint occurrences of temperature and precipitation at 78 stations and of temperature and relative humidity extremes at 31 sites, assessing the spatial and temporal variability of these extreme events in Florida’s tropical and subtropical low-lying regions. Influences of climate variability attributed to the El Niño Southern Oscillation (ENSO) on compound extremes are also evaluated using temporal windows that coincide with its two main phases (i.e., warm and cool). Preliminary findings indicate an increase in mean joint occurrences across several sites, suggesting an emerging trend that warrants further investigation. Nonparametric statistical tests confirm significant changes across two distinct temporal windows. The analysis reveals non-uniform patterns in compound extremes, influenced by both climate change and regional factors, such as the presence of large water bodies and wetlands. Notably, increases in the Heat Index (HI) highlight the growing risks to human health and the rising energy demand in the region. These findings underscore the need for further research to fully understand the spatial and temporal dynamics of compound extremes amid ongoing climate change.
How to cite: Teegavarapu, R. and Melendez, V.: Spatial and Temporal Changes in Compound Extremes in a Tropical Region: Links to Climate Variability and Change., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20941, https://doi.org/10.5194/egusphere-egu26-20941, 2026.
Resilient water-resources planning needs long datasets that capture low-frequency variability and rare, high-impact events beyond the instrumental era. This is particularly important in the Greater Dublin Area (GDA), where increasing water demand and reliance on a small number of linked sources and ageing infrastructure increases exposure to prolonged rainfall deficits. With major investment planned for new supplies and transfers, drought risk should be assessed against a long baseline. Here we develop a multi-method, multi-source reconstruction that extends monthly precipitation totals for the GDA back to 1748, and summer (MJJA) precipitation back to 1200.
Observations for the GDA are derived from the newly developed long-term gridded rainfall product produced by Met Éireann, the national meteorological agency. This extended gridded dataset integrates observations recovered through various citizen science and data rescue initiatives. For the GDA, we further extend monthly precipitation back to 1748 using two complementary approaches. First, we apply statistical reconstruction using physically interpretable circulation and hydroclimate predictors, including long sea-level pressure (SLP) series, pressure-gradient indices, and teleconnection modes. Models are calibrated separately for each calendar month using Lasso regression and random forests and uncertainties estimated using bootstrap resampling. Second, we scale monthly and annual precipitation anomalies for the period 1711-1977 compiled in a UK Met Office Branch Memorandum (No. 77) by Jenkinson et al. (1979) to observed GDA precipitation. This unpublished series combines early instrumental observations with documentary weather diaries (quantified via a graded wet–dry ranking scheme), drawing on UK regional series when Irish data are sparse and increasingly using Irish station records from the late 18th century onwards. Drought events identified from the reconstructed rainfall series are further verified using documentary sources, including the Irish Drought Impacts Database (1733-2019) derived from newspaper records, linking meteorological drought to locally reported impacts.
Finally, to extend the series further back, UK-based tree-ring records (oak cellulose δ¹⁸O) are used to reconstruct MJJA precipitation back to 1200. These data from central England are calibrated to the GDA using variance scaling and assessed with split-period verification. Together, these evidence streams provide a basis for a multi-centennial precipitation series and drought catalogue for the GDA, suitable for water-resources assessment and planning.
How to cite: Horvath, C.: Integrating Historical, Proxy, and Documentary Evidence for a Multi-centennial Drought Reconstruction for the Greater Dublin Area, Ireland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11294, https://doi.org/10.5194/egusphere-egu26-11294, 2026.
Understanding hydrological sequential extremes is a major contemporary challenge, particularly when droughts and floods increasingly occur in close succession. Recent studies have primarily focused on identifying consecutive and compound drought-flood events and their characteristics; however, the causal mechanisms linking these events remain largely unexplored. In this study, we investigate the causal relationships between hydrological droughts and subsequent floods, as well as their antecedent conditions, at the catchment scale. Building on established approaches for identifying drought–flood events, we extend the analysis by using the PC (Peter–Clark) algorithm to uncover causal dependencies between the characteristics of pre-event, streamflow drought, transition, and flood phases (Abbasizadeh et. al., 2025). To apply causal discovery, we first identify drought–flood events from 30 years of observed streamflow records across a large sample of catchments in the United States (Rakovec et. al., 2019). We characterize streamflow droughts by their duration and deficit, and floods by their volume, peak discharge, and duration. Using flux variables, namely precipitation, actual and potential evapotranspiration, and baseflow, as well as the state variable terrestrial water storage, we then identify anomalies during the pre-event, drought, and transition phases that lead to flooding. Actual and potential evapotranspiration, baseflow, and terrestrial water storage are derived from the mesoscale Hydrologic Model (mHM) simulation. The catchment characteristics are also included as the potential time-independent drivers of drought-flood events during different phases. Then the causal discovery method is applied to the pool of drought-flood events derived from all catchments to identify the causal links and their strength between variables. Our results reveal distinct causal links across the different phases, clarifying the conditions under which droughts either amplify or suppress the flood characteristics. These findings advance the understanding of compound and consecutive hydrological drought-flood extremes.
References:
Abbasizadeh, Hossein, Petr Maca, Martin Hanel, Mads Troldborg, and Amir AghaKouchak. "Can causal discovery lead to a more robust prediction model for runoff signatures?." Hydrology and Earth System Sciences 29, no. 19 (2025): 4761-4790.
Rakovec, Oldrich, Naoki Mizukami, Rohini Kumar, Andrew J. Newman, Stephan Thober, Andrew W. Wood, Martyn P. Clark, and Luis Samaniego. "Diagnostic evaluation of large‐domain hydrologic models calibrated across the contiguous United States." Journal of Geophysical Research: Atmospheres 124, no. 24 (2019): 13991-14007.
How to cite: Abbasizadeh, H., Rakovec, O., and Maca, P.: Causal Relationships in Sequential Drought–Flood Events Across Multiple Catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10554, https://doi.org/10.5194/egusphere-egu26-10554, 2026.
When floods and droughts occur successively before the river catchment is able to recover, their socio-economic and environmental impacts are amplified. While the drivers of individual extremes are well understood, the spatial connections and timescales of transitions between occurrences of high and low flow spells remain understudied, especially regarding their implications for operational risk management. Here we detect and characterize high and low flow spells at the catchment (local) scale as well as at the regional (decision-making) scale of operational flood forecasting centers. We use daily streamflow data from 643 catchments of the CAMELS-FR dataset in France over the 1970-2021 period, and investigate the occurrences of such spells within the 17 regional centers of the national forecasting service (hereafter called ‘SPC’, for ‘Service de Prévision des Crues’ in French). We initially use a mixed threshold approach combined with baseflow estimation as an indicator for catchment recovery to detect the spells and then analyze their frequency, duration, temporal transition and spatial connections (spatially compounding events). Our results show that consecutive occurrences of the same spell type are more predominant, with consecutive high flow spells being more common. Transitions occurring in less than a month from low to high flows show distinct spatial variability, with the shortest transition durations concentrated in the regions of the Rhone-Mediterranean and Rhine-Meuse river basins. These transitions mainly occur in autumn and early winter. On the other hand, transitions from high to low flows are typically slow, developing over more than 90 days. In addition, it is identified that the SPC Alpes du Nord (located in the Rhone-Mediterranean region) shows transition frequencies above the national mean for all transition types, while the SPC Bassin du Nord (in northern France) has lower frequencies in all transition types. We also applied a synchronization approach to investigate spatially compounding events by identifying pairs of catchments with concurrent high flow spells, low flow spells, and opposing spells (e.g. when one catchment is experiencing a high flow spell, while another is experiencing a low flow spell). The analysis allowed us to detect regions with high spatial connectedness, as well as patterns of inter- and intra-annual variability of spatially connected spells. Finally, perspectives on the application of the developed methodology at the European scale are discussed.
This work is funded by EU Horizon Europe project MedEWSa (Mediterranean and pan-European forecast and Early Warning System against natural hazards) under Grant Agreement 101121192.
How to cite: Guimarães, G. M., Ramos, M.-H., and Pechlivanidis, I.: Understanding temporal transitions and spatial connections of high and low flow spells at local and regional scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7343, https://doi.org/10.5194/egusphere-egu26-7343, 2026.
Previous work has shown that geomorphic changes can drive changes in channel conveyance capacity that affect flood hazard (Slater et al., 2015; Pinter et al., 2008). However, these prior studies have tended to frame evolving flood hazard as a monotonic trend (in response to gradual channel aggradation or degradation), without detailed consideration of the actual trajectory of change. Here we suggest that the evolution of flood hazard (here represented by changes in water level for a constant discharge) in river systems is sometimes expressed as behaviours consistent with geomorphic punctuated equilibrium (Phillips, 2006), whereby periods of relative stability and/or gradual change are interrupted by abrupt shifts in water levels.
To illustrate this, we present an analysis of the frequency and magnitude of abrupt shifts in water level for constant discharges (so-called specific gauge analysis) using long term (>30 year) records at more than 120 US gauging stations. At each station we first identify linear trends (via Mann-Kendall (MK) testing) in the water level time series, before identifying abrupt discontinuities using a Pruned Exact Linear Time (PELT) algorithm, creating a robust framework for detecting multiple regime shifts within each time series.
Preliminary results reveal that, even for our gauging station study sites – typically considered to be geomorphically stable – both gradual adjustments and abrupt shifts in water level are common across a wide range of return-period flows. We also explore how the prevalence of these instabilities varies in response to driving factors such as sediment connectivity, channel confinement, and flow regulation. These findings have implications for flood risk management, suggesting that static flood maps may be insufficient in dynamic landscapes to represent the actual risks posed to exposed populations and assets.
References:
Phillips, J. D. (2006). Evolutionary geomorphology: thresholds and nonlinearity in landform response to environmental change. Progress in Physical Geography, 30(4), 431-447.
Pinter, N., et al. (2008). Cumulative impacts of river engineering, Mississippi River, USA. Geomorphology, 101(1-2), 147-160.
Slater, L. J., Singer, M. B., & Kirchner, J. W. (2015). Hydrologic versus geomorphic drivers of trends in flood hazard. Geophysical Research Letters, 42(2), 370-376.
How to cite: Gasparotto, A. and Darby, S.: Punctuated Equilibrium in River Systems: Quantifying Abrupt Hydraulic Instability Across Water Level Timeseries, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13420, https://doi.org/10.5194/egusphere-egu26-13420, 2026.
The increasing frequency of hydro-meteorological extremes in Germany has resulted in significant economic and environmental losses, totaling at least EUR 145 billion in damages between 2000 and 2021. While these events affect the nation at large, the vulnerability of Germany’s administrative districts varies drastically due to local differences in topography, weather patterns, and land-use characteristics. Adaptation to climate risks requires both risk identification and the strategic placement of Decentralised Water Retention Measures (DWRM) capable of buffering both excess and scarcity. We present a Multi-Criteria Decision Analysis (MCDA) framework that ranks the districts in Southern Germany (Baden-Württemberg and Bavaria) based on their climate vulnerability. The proposed framework synthesizes diverse hydro-meteorological indicators into a single index, allowing for an objective comparison of regional risks. This study utilizes the comprehensive dataset provided by the Climate Service Center Germany (GERICS), consisting of 85 regional climate model simulations, evaluating future hydro-meteorological trajectories under three different Shared Socioeconomic Pathway (SSP) emission scenarios and combines an Entropy Weight Method to objectively determine the importance of each indicator by calculating its information utility based on data variance across the study area. Thereafter, the vulnerability of each district was calculated based on the geometric distance to theoretical ideal solutions using the TOPSIS method. The results of this analysis aim to identify persistent hotspots that consistently rank as highly vulnerable across all climate trajectories and emission scenarios. These identified districts will be spatially mapped and linked to their respective hydrological catchments to facilitate future high-resolution hydrological modelling and site-specific engineering assessments.
Using the screening outputs, three representative case study areas are selected in which the water balance will be modelled in detail. Building on this, the aim is to develop a coupled simulation tool, based on SWAT+, that identifies potential decentralized retention areas and evaluates their effectiveness as a combined measure for drought and flood prevention. Once development is complete, the tool will be made publicly available to enable broad application in water resource management and promote knowledge transfer between research and practice. Future results will be disseminated through policy briefs, scientific publications, and participatory workshops to engaged decision-makers and stakeholders from administration and planning at an early stage in the development of sustainable water strategies.
How to cite: Ghosh, S., Holzer, J., and Disse, M.: Spatial identification of vulnerable regions for combined flood and drought prevention in Southern Germany , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20588, https://doi.org/10.5194/egusphere-egu26-20588, 2026.
Africa’s climate is shaped by a complex interplay between atmospheric and oceanic systems that affect each part of the region rather differently with distinct seasonality. The northernmost and southernmost areas of the continent have a Mediterranean-type climate, with dry-hot summers and moist-moderate winters, which results in distinct seasonality in the hydrological cycle and semi-aridity [1, 2]. The Sahara, a large desert in northern Africa, is located in the subtropics and has an arid climate characterised by highly variable precipitation patterns and water scarcity. A large part of sub-Saharan Africa, however, is under the influence of monsoonal systems, whose dynamics and seasonality are heavily influenced by large-scale meridional temperature gradients that lead to cross-equatorial energy imbalance and shifts of the intertropical convergence zone [3], that is, a narrow low-pressure band where moist air ascends and results in heavy precipitation.
Monsoonal systems are highly crucial for the tropical African countries' socio-economic development, particularly their agriculture, ecosystem, and energy systems. The interaction between monsoonal systems and the large-scale modes of variability, such as the El Niño-Southern Oscillation and the Indian Ocean Dipole [4], gives rise to strong spatial precipitation gradients and pronounced year-to-year variability.
As a result of human-induced warming, the atmospheric and oceanic circulation patterns are expected to change, which can lead to changes in precipitation patterns and distribution, increased evapotranspiration, and increased extreme events such as floods and droughts. In this work, we monitor and project extreme events over Africa, with a particular focus on droughts, to understand how drought propagates from one system to another. We consider drought not as a single event but as a continuous, evolving process [5] with interconnected impacts on the hydrological, agricultural, socio-economic, and overall African energy systems across different timescales.
To address this, we use observational records, reanalysis, climate model historical simulations and projections of different scenarios, and examine different meteorological and impact-based indices to 1) understand the climatology of Africa in the historical/present-day period, 2) analyse the spatiotemporal changes in its hydroclimate within the historical records and in the coming decades, and 3) identify drought events in the past and in the coming decades to check if, and to what extent, Africa's infrastructure can buffer the impacts of extreme events.
References:
[1] Giorgi, F. (2006). Climate change hot-spots. Geophys. Res. Lett, 33, 8707. https://doi.org/10.1029/2006GL025734
[2] Seager, R., et al. (2019). Climate Variability and Change of Mediterranean-Type Climates. Journal of Climate, 32(10), 2887–2915. https://doi.org/10.1175/jcli-d-18-0472.1
[3] Nicholson, S. E. (2018). The ITCZ and the Seasonal Cycle over Equatorial Africa. Bulletin of the American Meteorological Society, 99(2), 337–348. https://doi.org/10.1175/BAMS-D-16-0287.1
[4] Hoell, A., & Funk, C. (2013). The ENSO-Related West Pacific Sea Surface Temperature Gradient. Journal of Climate, 26(23), 9545–9562. https://doi.org/10.1175/JCLI-D-12-00344.1
[5] Van Loon, A. F., et al. (2024). Review article: Drought as a continuum – memory effects in interlinked hydrological, ecological, and social systems, Nat. Hazards Earth Syst. Sci., 24, 3173–3205, https://doi.org/10.5194/nhess-24-3173-2024
How to cite: Tootoonchi, R. and Castelletti, A.: Drought Continuum in Africa: A Multidimensional Assessment of Meteorological, Hydrological, Agricultural, and Socioeconomic Drought, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9790, https://doi.org/10.5194/egusphere-egu26-9790, 2026.
A critical challenge in catastrophe modeling is the requirement for high-resolution stochastic event sets that span tens of thousands of years to accurately sample extreme tail risks. Traditional weather generators often rely on simplified statistical assumptions that struggle to maintain complex multi-variable physical consistencies and to realistically capture climate change. Conversely, dynamical downscaling of General Circulation Models (GCMs) is computationally prohibitive to generate the multi-millennial simulations covering large domains required for robust risk assessment.
We present a computationally efficient modeling chain that leverages generative diffusion models to overcome these limitations. Our framework is rooted in GCM runs, allowing it to account for climate change and capture its impacts across variables, space and varying global warming levels. Specifically, we leverage the CESM2 Single Model Initial-condition Large Ensemble (SMILE) to sample internal natural variability and generate events more extreme than in the historical record. The methodology employs an emulator based on autoregressive video diffusion to produce synthetic GCM-resolution atmospheric states (see presentation EGU26-19946), enabling us to go beyond the length of the SMILE time series. The emulated fields are processed through a diffusion model trained on reanalysis data downscaling them to ~10km resolution (see presentations EGU26-19822 and EGU26-20546). This modeling chain preserves seasonal dependencies and atmospheric patterns while providing the stable, multi-decadal sequences necessary to generate river flow time series using the process-based Wflow hydrological model (see presentation EGU26-4924).
We prove the validity of this framework over the United Kingdom, where we show it successfully reproduces event frequencies and severity, and generates convincing reconstructions of historical events. We discuss the most extreme floods of the simulations and critically assess their realism. Our modelling chain illustrates that the use of machine learning (diffusion models) enables in-house hydroclimatic modelling from the GCM to catchment scale over periods and domain sizes much larger than previously possible.
How to cite: Addor, N., Hoch, J., Lord, N., Lucas, C., Marshall, A., Moraga, J. S., and Wing, O.: Generating extreme floods using multi-millennial high-resolution simulations: A proof-of-concept over the UK, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19994, https://doi.org/10.5194/egusphere-egu26-19994, 2026.
Transitions from dry to wet states challenge water management practices and can lead to severe impacts. Recent studies have investigated dry–wet transitions from a meteorological perspective and focused on the transition itself. By contrast, how the hydroclimatic system behaves before and after the transition remains understudied. Here, we study where and why hydroclimatic transitions in Europe display asymmetric pre- and post-transition behavior using large-sample climate model simulations under pre-industrial, historical, and future climates. Our results show that hydroclimatic transitions across Europe are frequently characterized by asymmetric behavior around regime shifts, rather than balanced recovery. This indicates that some regions experience rapid recovery following dry–wet transitions, while others exhibit persistent anomalies beyond the transition itself. This pattern suggests that hydroclimatic recovery is conditioned by antecedent states, with systems retaining memory of prior dryness or wetness. As a result, transitions may be followed by either abrupt intensification of wet conditions or slow and incomplete recovery, implying differing propensities for post-transition flood amplification or prolonged drought persistence.
How to cite: Khosh Chehreh, M., Ragno, E., and De Michele, C.: Asymmetric Hydroclimatic Transitions in Europe: Insights into Recovery and System Memory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19758, https://doi.org/10.5194/egusphere-egu26-19758, 2026.
Precipitation lapse rates (PLRs) describe how precipitation varies with elevation. PLRs strongly influence the water balance of high-elevation catchments and affect the seasonal dynamics of snow accumulation and related snowmelt runoff, as well as the longer-term mass balance of glaciers. Despite their importance, precipitation variations with elevation remain poorly understood due to the complex and highly localized nature of orographic precipitation as well as the limited availability of high-elevation precipitation observations. Consequently, streamflow simulation in mountainous environments is challenging, and many hydrological studies rely on the simplifying assumption of a constant (usually positive) PLR.
The representation of PLR in both the input data and the hydrological models plays a key role in streamflow simulations. Here, we used a combination of long synthetic time series from a stochastic weather generator (GWEX) and a hydrological catchment model (the HBV model) to study the influence of PLR on runoff simulations for several Swiss catchments. To better understand the influence of PLR on the simulations, particularly on flood estimates, we conducted two experiments. In the first experiment, we varied the PLR parameter in HBV between 0% and 10% (0%, 2.5%, 5%, 7.5%, and 10%). This parameter redistributes the mean catchment precipitation from GWEX between elevation zones without altering the total precipitation amount. In the second experiment, we applied the same PLRs to first adjust precipitation for the difference between station and mean catchment elevation, before interpolation to mean areal precipitation using Thiessen weights. For each simulation run, GWEX inputs were adjusted using the same PLR that was applied in the hydrological model. Through these experiments, we assessed the sensitivity of flood estimates to changes in PLRs applied solely within the hydrological model and to the combined application of PLRs in both precipitation input and hydrological model.
Our findings show variable responses in the monthly water balance, flood seasonality, and changes in the flood estimates for both experiments, reflecting differences in catchment characteristics. This evaluation highlights the importance of PLRs in hydrological studies and demonstrates that the use of a fixed PLR can be misleading. Instead, PLR assumptions should be context-dependent and carefully considered in hydrological applications.
How to cite: Kritidou, E., Kauzlaric, M., Vis, M., Staudinger, M., Seibert, J., and Viviroli, D.: The influence of precipitation lapse rate in flood estimates using long continuous simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6562, https://doi.org/10.5194/egusphere-egu26-6562, 2026.
In summer of 2025, an extreme drought occurred in the east cost region in South Korea. The Gangneung city encountered a critical shortage of water supply from July to mid-September and a ‘state of disaster’ was declared by the government. The Obong reservoir of 100 km2 catchment and 1.4 million m3 storage is operated for the water supply for 200,000 residents and 480 ha of irrigated paddy fields. This severe drought was ended with abnormal rainfall after mid-September and reservoir storage was returned to normal levels.
To better understanding the hydrologic response in small mountainous catchment, the 25 years of climate and hydrologic data (2000~2025) analysed and daily record of water supply (2023~2025) for municipal and irrigation from the reservoir were collected. The catchment characteristics including topography, soil type and shallow aquifer properties were also analysed using DEM and digital soil and land-use map. The hydrological dynamics including soil moisture and groundwater level changes are simulated using physically-based hydrologic model, DWAT (Dynamic Water Resources Assessment Tool). The DWAT is run on a daily time step to generate hydrologic processes in the catchment from the rainfall and climate data (temperature, humidity and wind velocity, sunshine hours).
The hydrologic behaviour was simulated from 2000 to 2025 and the simulations result showed that the dramatic changes of soil moisture after mid-September rainfall and which subsequently increase of streamflow from the catchment. The simulation results highlighted the strong influence of antecedent soil moisture conditions on catchment response. During the drought period, soil moisture was critically low, limiting runoff generation even during heavy rainfall events. For example, on September 13, even the daily rainfall was exceeded 100 mm, the streamflow was not increased significantly because of the low soil moisture in the catchment. Once the soil was saturated, interflow increased streamflow, followed later by baseflow from the shallow aquifer. The water storage of Obong reservoir was lowest to 11.5% in mid-September and dramatically recovered over 90% by 300 mm of rainfall until the early of October.
This study demonstrates the value of long-term hydrologic modelling for understanding drought resilience in small mountainous catchment. The DWAT simulations provided insights into the interactions between rainfall, soil moisture, streamflow, and groundwater, highlighting the critical role of antecedent conditions in shaping hydrologic response. Moreover, the case of Gangneung in 2025 illustrates how extreme droughts may be rapidly reversed by anomalous rainfall, yet such reliance on unpredictable events poses significant risks for water resource management.
This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Water Management Program for Drought Project, funded by Korea Ministry of Climate, Energy and Environment(MCEE).(2022003610002)
How to cite: Kim, H., Jang, W., Jang, C., and Kim, D.: Modelling hydrological dynamics under dramatic shift from drought to flood in the small mountainous catchment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15972, https://doi.org/10.5194/egusphere-egu26-15972, 2026.
Drought develops gradually as a consequence of sustained rainfall shortages and extends through the land surface system, leading to reductions in soil moisture and impacts on agricultural production. In regions such as India, where the climate is strongly influenced by the monsoon, clarifying the linkage between meteorological drought conditions and subsequent agricultural drought is essential for improving drought assessment and early warning capabilities. This study develops a spatio-temporal framework to examine drought propagation by combining statistical drought indices with network-based analysis. Meteorological drought is quantified using the Standardized Precipitation Index (SPI) at multiple accumulation time scales to represent short- and long-term rainfall anomalies. Agricultural drought is represented using the Standardized Soil moisture Index (SSI), which is calculated from soil moisture anomalies. Drought events are identified using run theory, from which their onset, duration, and severity are determined. The relative timing between meteorological and agricultural drought is evaluated by examining lagged correlations between SPI and SSI, which allows the response delay of agricultural drought to be estimated for different regions. The observed lag patterns differ across space, reflecting variations in soil properties, local climate conditions, and interactions between the land surface and the atmosphere.
To assess spatial coherence, separate single-layer spatial networks are constructed for SPI and SSI, where grid cells represent nodes and statistically significant correlations define network connections. Network measures such as degree and betweenness centrality are applied to determine the regions that exert the greatest influence on drought connectivity. The analysis shows that meteorological drought tends to form more widespread and spatially coherent connectivity patterns, whereas agricultural drought exhibits stronger spatial contrasts linked to land-surface processes. These differences indicate that drought does not propagate uniformly but follows region-specific pathways shaped by local response times within the hydrological system. The proposed framework improves understanding of drought evolution from rainfall deficits to soil moisture stress and provides useful insights for drought monitoring, early warning, and climate-resilient agricultural planning.
Keywords: Standardized Precipitation Index, Standardized Soil moisture Index, Drought propagation, Network analysis
How to cite: Sunil, A. and Singh, S.: Spatio-Temporal Analysis of Drought: From Identification to Propagation Pathways, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9706, https://doi.org/10.5194/egusphere-egu26-9706, 2026.
Design flood estimates are essential for the construction and maintenance of hydrological infrastructure. In practice, flood frequency distributions and their parameters are estimated using limited samples of observed data. However, both the estimation process and the data themselves are subject to considerable uncertainty. One of the key challenges in this context is the occurrence of extreme floods. For example, in September 2024, a flood ranking among the most significant hydrological events of the past several decades occurred in the Czech Republic. The Jeseník region was the most affected area. Despite the hydrological drought conditions preceding the event, the flood return period in this region was estimated to be as high as 500 years. An interesting fact is that this region experienced a second extreme flood within less than 30 years (since July 1997). This recurrence highlighted the need to verify existing design flood estimates and proceed with their revision. Frequency analysis of observed annual peak discharges indicates that the design flood with a 100-year return period may be underestimated at some gauging stations in the region by 10–20%. On the contrary, data from recent decades suggest an overestimation of less extreme desing floods with return periods of approximately 1 to 10 years. Consequently, an innovative approach to design flood estimation that incorporates both of these findings is currently being developed.
How to cite: Svoboda, V. and Barešová, L.: Design flood estimation and its uncertainty under current climate in the Czech Republic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18045, https://doi.org/10.5194/egusphere-egu26-18045, 2026.
Recent decades have been associated with an apparent intensification of hydrological extremes across the Madeira River Basin (Amazonia), reinforcing the need for forecasting frameworks that are reproducible, leakage-safe, and operationally defensible. An integrated machine-learning workflow is implemented to forecast the downstream Standardized Streamflow Index (SSI-12) at gauging station 15700000, using a strictly time-ordered monthly dataset and an explicitly controlled validation protocol. The supervised learning design is defined via forward target shifting (h = 1) and explicit representation of hydrological memory through antecedent lag terms (1-12 months), consistent with the persistence embedded in accumulated standardized indices. Data preparation comprises temporal harmonization, conversion to consistent numeric formats, and reconstruction of residual gaps through KNN imputation to better preserve multivariate covariability in predictor space. A parsimonious modeling pipeline is adopted, combining standardization (training statistics only) with mutual-information-based feature screening to enforce predictor compactness and reduce redundancy. Hyperparameters and feature subset size are optimized via RandomizedSearchCV under TimeSeriesSplit cross-validation, with NSE used as the primary refit criterion. Final fitting is refined through external early stopping on a held-out validation segment, monitoring a robust Huber loss to stabilize training under heteroscedastic conditions. Out-of-sample skill assessed through RMSE, MAE, R2, NSE, and KGE indicates strong predictability and close phase agreement between forecasts and observations. Nevertheless, a persistence-type baseline remains superior on validation and test partitions, underscoring the pronounced short-term autocorrelation intrinsic to SSI-12 and setting a stringent benchmark for incremental gains. Residual behavior under extremes further indicates heteroscedasticity and systematic peak attenuation, motivating extreme-aware refinements centered on residual learning relative to persistence, event-centric feature engineering incorporating exogenous hydroclimatic drivers, and tail-sensitive optimization to improve fidelity during high-impact episodes.
How to cite: Camarano Lüdtke, J., Melo Brentan, B., and Ferreira Rodrigues, A.: MLP-based hydrological forecasting in the Madeira River Basin, Amazonia: a prelude toward robust modeling of hydrological extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15273, https://doi.org/10.5194/egusphere-egu26-15273, 2026.
The Amazon Basin exhibits high hydrological variability and has experienced, in recent decades, an increase in the frequency, intensity, and duration of extreme drought and flood events, with significant impacts on ecosystems, water availability, and socioeconomic activities. Understanding not only the isolated occurrence of these events but also their spatiotemporal evolution and interconnections across different regions of the basin remains a major scientific challenge. In this context, this study proposes an approach based on the Standardized Precipitation–Evapotranspiration Index (SPEI) and complex network theory to investigate the spatiotemporal dynamics of droughts and floods in the Amazon. The analysis is based on monthly time series of precipitation and actual evapotranspiration for 43 Amazonian catchments obtained from the CAMELS-BR dataset. Precipitation is represented using data from the CHIRPS product, while actual evapotranspiration is derived from the GLEAM and ERA5-Land datasets. From the climatic water balance, the SPEI accumulated over a 12-month timescale (SPEI-12) is computed, allowing the characterization of medium- to long-term hydrological anomalies. Drought and flood events are identified using widely adopted thresholds in the literature, and additional attributes such as duration, accumulated intensity, and recovery rate are derived to assess the severity and persistence of hydrological extremes. In the subsequent stage, the SPEI time series are analyzed comparatively to quantify similarities in hydrological behavior among catchments and to identify common patterns in the temporal evolution of drought and flood events. These relationships are incorporated into the construction of dynamic graphs, in which each catchment is represented as a node and the connections reflect hydrological proximity among the time series and extreme events characteristics. The temporal analysis of the graphs enables the investigation of how connectivity among catchments reorganizes during dry and wet periods, as well as the identification of regional groupings and catchments that play structurally important roles in the Amazonian hydrometeorological network. By integrating the characterization of hydrological extremes with dynamic network modeling, this study provides an innovative framework for interpreting the complexity of hydrological variability in the Amazon. The expected results contribute to advancing the understanding of the spatiotemporal evolution of droughts and floods and provide support for the development of monitoring, forecasting, and risk management strategies in one of the regions most vulnerable to climate change.
How to cite: Kolanski, M., Maia, T., Brentan, B., and Rodrigues, A.: Spatiotemporal evolution of drought and flood events in the Amazon: An approach based on complex network theory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5966, https://doi.org/10.5194/egusphere-egu26-5966, 2026.
The estimation of the return levels of floods is constrained by sparse and quality-limited hydrological observations in West Africa, even though floods remain among the most damaging natural hazards in the region. Regional Flood Frequency Analysis (RFFA) provides a pathway to estimate design floods at ungauged catchments, yet the diversity of available approaches calls for a systematic comparison. We assess whether flood quantiles can be reliably regionalized across West Africa using an unprecedented dataset of 211 near-natural catchments. This study compare a Direct Regression Approach (DRA) with three index-flood methods based on spatial proximity, Principal Component Analysis (PCA), and Canonical Correlation Analysis (CCA), all of which are implemented using both statistical and machine-learning models. Evaluation of model performance using relative bias (rBias) and mean absolute relative error (MARE) indicates that index-flood-based approaches consistently outperform DRA. Among all combinations, the CCA–SVR framework achieves the highest accuracy (rBias = -0.03; MARE = 0.21) for both 20- and 50-year flood quantiles. These findings provide robust guidance for flood design in data-scarce environments and support more resilient flood risk management across West Africa.
How to cite: Diop, S. B., Tramblay, Y., Bodian, A., Dieppois, B., and Ouarda, T. B. M. J.: Comparison of regional flood frequency analysis methods for ungauged catchments in West Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1538, https://doi.org/10.5194/egusphere-egu26-1538, 2026.
Recent consecutive dry summers in North Western Europe caused significant impacts across ecosystems and socio-economic sectors. Climate change is expected to increase the frequency and severity of drought and its impacts. To mitigate drought impacts in the future, it is crucial to improve our understanding on the effects of climate change on drought and its impacts.
A range of meteorological and hydrological drought indicators exists to quantify droughts. Depending on the drought impact of interest, different indicators aggregated over different timeframes (temporal aggregation scales) may be relevant. However, in climate change impact assessments, indicators currently are often used without assessing their relevance for the impact of interest. As a result, it often remains unclear which indicator and associated temporal aggregation scale is most appropriate for a given drought impact in a particular region.
We apply a bottom-up, data-driven approach to determine which hydrometeorological drought indicators are relevant and to identify their appropriate temporal aggregation scales for different drought impacts in the Dutch-German border region. Starting with drought impacts, such as agricultural yield loss, we derive hydrometeorological indicators and their temporal aggregation scales. For example, we analyse the correlation between groundwater table depths with varying temporal aggregation scales and crop yield loss simulated with the WaterVision Agriculture tool. Among the tested temporal aggregation scales (1 to 12 months), a five-month aggregation of groundwater table depth shows the highest correlation with agricultural yield loss. This shows that the groundwater table depth aggregated over the final five months of the cropping season is an important hydrological drought indicator for agricultural yield loss in the Dutch-German border region. Next steps include extending the analysis to other drought impacts and linking hydrological and meteorological indicators.
This analysis improves understanding on the relevance of various hydrometeorological indicators and associated temporal scales for different drought impacts and helps in assessing the effects of climate change on drought impacts in the future.
How to cite: ten Berge, A., Booij, M., and Krol, M.: Data-based hydrometeorological drought indicators and appropriate temporal aggregation scales for studying drought impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16610, https://doi.org/10.5194/egusphere-egu26-16610, 2026.
Understanding the drivers of hydrological change is essential for sustainable water governance. This study examines long-term hydroclimatic variability and drought evolution in the Kävlingeån Basin, southern Sweden, over the period 1970–2020. Trend analyses of precipitation, runoff, and potential evapotranspiration indicate increasing atmospheric water demand accompanied by declining runoff, suggesting an overall tendency toward drier conditions. Drought indices (SPEI-03 and SPEI-12) further reveal increasing drought persistence and pronounced seasonal asymmetry. Monthly trend analysis of SPEI-03 shows significant drying in March and April, coinciding with the onset of the agricultural growing season, which may pose challenges for crop production and irrigation management in this predominantly agricultural basin. Decadal variability analysis towards wet and dry events indicates that SPEI-12 has shifted toward drier conditions since the 1970s, characterized by a reduction in annual mean wet events and an increased frequency of dry events. In contrast, SPEI-03 exhibits no clear long-term trend, suggesting that short-term water balance variability has remained relatively stable.
Furthermore, attribution analysis of multi-year runoff variations suggests that non-climatic factors potentially contribute more to the observed changes than climatic drivers, including precipitation and potential evapotranspiration, indicated by a set of Budyko framework based analysis at yearly and monthly time scale. This finding indicates that human activities have likely played a substantial role in reshaping the hydrological balance of the basin.
How to cite: An, D. and Persson, K.: Hydroclimatic Variability and Its Implications for Drought and Runoff in the Kävlingeån Basin, Southern Sweden (1970–2020), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11952, https://doi.org/10.5194/egusphere-egu26-11952, 2026.
How far can hydrological models be pushed before they break down? This study explores that question by exposing a range of modelling approaches—simple conceptual models such as the Direct Runoff Model, more complex conceptual models like HBV-Light and LARSIM, as well as a data-driven model based on LSTM networks —to both real and hypothetical rainfall extremes. Beyond reproducing extreme rainfall events that caused historical floods (Ahr, Münster, and Elbe), the models are subjected to deliberately exaggerated and synthetic rainfall scenarios that challenge the physical and conceptual limits of model design. These “stress runs” reveal how each model responds when rainfall becomes exceptionally intense, prolonged, or short but extreme — conditions that are increasingly relevant under a changing climate.
As a case study, results for the Ahr catchment at an hourly resolution are presented, including analyses of different initial states and calibration periods. By examining model robustness—the ability to produce physically plausible runoff across a wide spectrum of extreme conditions—and identifying failure modes, the study uncovers hidden sensitivities, structural biases, and nonlinear behaviors that standard validation approaches may overlook. The goal is to rethink how robustness is assessed and to advance hydrological models capable of withstanding the extremes of the future.
How to cite: Shehu, B., Tanzeglock, P., Yeste, P., Voit, P., Heistermann, M., and Bronstert, A.: Breaking the Limits: Stress Testing Hydrological Models Beyond Observed Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20212, https://doi.org/10.5194/egusphere-egu26-20212, 2026.
How to cite: Nakamura, S., Arase, N., Ann Sanchez, P., and Ohara, M.: A Hierarchical Sociohydrological Model Incorporating Spacial Heterogeneity for Flood Risk Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15821, https://doi.org/10.5194/egusphere-egu26-15821, 2026.
The Mediterranean Basin is a region naturally prone to drought due to its climatic variability. These variations are being exacerbated by the current climate change situation, with projections agree that the frequency and severity of these extreme events will increase. Paradigmatically, this region has based its socioeconomic development on activities directly related to water resources, such as agriculture and tourism. In addition, the Mediterranean basin is delimited by mountain ranges close to the sea that draw different-sized catchments where water resources availability is directly linked to snow presence. Therefore, snow dynamics need to be considered when analyzing droughts. Cold and warm winter droughts over these mountains have special characteristics since winter temperatures are around zero and conditions both snow accumulation and ablation directly.
However, the general drought indices, SPI (Standardized Precipitation Index) or SPEI (Standardized Precipitation-Evapotranspiration Index), which are the most widely used tools to characterize droughts, do not explicitly account for snow. New snow drought indices have been proposed, for instance, the Standardized Snow Water Equivalent Index (SSWEI). They are, on the one hand, based on snow variables typically derived from modelling and are subject to large uncertainties over Mediterranean mountain catchments; and, on the other hand, focus mainly on the ablation process. This work proposes to define a new drought index, introducing the concept of snowfall drought in Mediterranean mountain regions. Then, snowfall is the target variable used to define the drought index, the Standardized Snowfall Index (SSNI). The index definition is based on the methodology already proposed when defining other drought indices, evaluating, in this case, eight different candidate distributions, and standardizing their probability using a Normal distribution. The aggregation time selected for the snowfall series was 12 months. HydroGFD3 bias-adjusted reanalysis data (daily time-step and 25 km spatial resolution) for precipitation and temperature during the period 1980-2024 are used in the study. Snowfall is determined using a variable temperature thresholding over the whole Mediterranean Basin (2266 catchments). The SSNI was evaluated against the SPI index to assess the differences in detecting drought between the two indices.
The results show that the candidate distribution selected differed depending on the location of the catchment. That is, the Gamma distribution was the best at capturing the snowfall drought dynamics in high elevation catchments, Log-Logistic in the eastern Mediterranean catchments, and Weibull in the western ones. The number of drought periods also differed spatially, ranging from 0 to 22 episodes with a duration between 0 and 20 months, and a clear relationship between both: the longer the duration, the smaller its frequency. In addition, this new index helps better quantify the effect of a snow deficit in the meteorological drought definition - from all months identified with a drought, 38% of them would not have been classified as drought months, only accounting for precipitation and not for snowfall - and consequently to better understand the drought propagation cascade over the region.
Acknowledgments: This work is part of the project CNS2023-145125, funded by MCIN/AEI/10.13039/501100011033 and European Union “NextGenerationEU”/PRTR.
How to cite: Pimentel, R., Torralbo, P., Raquel, G.-B., Marta, E., Andreu, A., and María José, P.: Assessing snowfall droughts in mediterranean mountain catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18016, https://doi.org/10.5194/egusphere-egu26-18016, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot A
EGU26-15655 | Posters virtual | VPS9
Assessing changes in flood inundation patterns using rainfall-controlled event analysisWed, 06 May, 14:36–14:39 (CEST) vPoster spot A
Flood hazard assessments are commonly based on rainfall magnitude and frequency; however, flood responses to similar rainfall intensities may change over time due to evolving land use, river morphology, and hydraulic controls. This study investigates temporal changes in flood inundation patterns between 2014 and 2024 over a highly flood prone urban coastal catchment in India under comparable rainfall forcing, with the objective of improving understanding of non-stationary flood behavior. Rainfall events were identified and grouped based on intensity and duration using long-term precipitation records. For each selected event, flood extents were mapped using satellite-based inundation detection implemented on cloud computing platforms, and, where appropriate, complemented by physically based hydraulic modeling. This combined rainfall–flood framework enables consistent inter-annual comparison of flood patterns under equivalent meteorological conditions. The methodological approach focuses on isolating the influence of landscape and hydraulic evolution on flood response by analyzing spatial characteristics of inundation independent of rainfall variability. By integrating remote sensing and hydraulic modeling within a long-term analysis, the study provides a transferable framework for assessing how flood behavior evolves in response to environmental and anthropogenic changes. This work is relevant to flood risk management and climate adaptation, particularly in rapidly changing river basins where traditional stationary assumptions may no longer be valid. The approach supports improved interpretation of historical floods and more robust planning under future uncertainty.
How to cite: Ghosh, M. and Karmakar, S.: Assessing changes in flood inundation patterns using rainfall-controlled event analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15655, https://doi.org/10.5194/egusphere-egu26-15655, 2026.
EGU26-12345 | Posters virtual | VPS9
Drought prediction and understanding the drivers of drought development using a machine learning approachWed, 06 May, 14:54–14:57 (CEST) vPoster spot A
Over the past decade, droughts have drawn increasing attention due to their substantial agricultural and economic consequences, particularly in the U.S. Great Plains area (e.g., the 2012 Central US event and the 2017 Northern Plains event). Although certain large-scale atmospheric and oceanic patterns are necessary for drought development, land- atmosphere interactions can play an important role in the intensification of drought conditions, especially for flash drought. This study aims to predict drought conditions over the U.S. Great Plains at 1-3-week lead times using a convolutional neural network (CNN) model. To forecast drought categories derived from the US Drought Monitor (USDM), the models are trained using multi-source atmospheric and land-surface variables, including 500 hPa geopotential height, precipitation, wind speed, surface radiation, humidity, and temperature from ERA5, soil moisture from Global Land Evaporation Amsterdam Model (GLEAM) and North American Land Data Assimilation System (NLDAS), and Normalized Difference Vegetation Index (NDVI) from satellite products. Model performance is evaluated to unravel the atmospheric and land-surface processes that drive droughts at different lead times and identify their relative contributions to drought development and intensification.
How to cite: Rippeteau, L. and Chen, L.: Drought prediction and understanding the drivers of drought development using a machine learning approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12345, https://doi.org/10.5194/egusphere-egu26-12345, 2026.
EGU26-16235 | ECS | Posters virtual | VPS9
Hydrological Whiplashes Over India: Patterns, Drivers, and RecurrenceWed, 06 May, 15:03–15:06 (CEST) vPoster spot A
Climate change is driving a marked intensification of hydrological extremes, including both droughts and floods. When these opposing conditions occur in close succession, known as hydrological whiplash, they generate compounded impacts on ecosystems, infrastructure, and human livelihoods. We analyze hydrological whiplash across India using observed streamflow data and simulations from the validated H08-CaMa-Flood model. The results indicate that nearly 90% of streamflow stations experienced at least one whiplash event, with drought-to-flood transitions being both more common and more abrupt than flood-to-drought shifts. These events are concentrated primarily during the monsoon season, but their occurrence has increased in the non-monsoon months in recent decades, particularly in high-elevation regions. Moreover, we find that whiplash events are becoming more frequent and more intense, while the interval separating dry and wet extremes is shrinking, signaling an escalation of hydrological volatility across the country. Together, these patterns underscore the need for strengthened monitoring, early warning capabilities, and adaptive water management strategies to reduce the growing risks associated with rapid hydrological transitions under a warming climate.
How to cite: Sharma, P. and Mishra, V.: Hydrological Whiplashes Over India: Patterns, Drivers, and Recurrence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16235, https://doi.org/10.5194/egusphere-egu26-16235, 2026.
