Europe has experienced extreme floods in recent decades. However, even larger floods are possible and must be considered in flood risk management1,2. Their characteristics can be clarified by analysing the largest documented historical floods. In Central Europe, the Magdalena Flood of July 1342 is usually considered the largest of the last millennium; however, knowledge of its characteristics is incomplete3-7. Here we show that 16 major flood events occurred across much of Europe between late 1341 and 1343. Four of these events had return periods of 500-1,000 years (the Magdalena, Bartholomew, Candlemas and Jacob Floods). Although Magdalena was thought previously to be the only extreme European flood in 13423,7, our new documentary dataset suggests that it formed part of a broader sequence. The year with the greatest number of extreme floods during the past 700 years was 1342, and 1343 ranks among the top ten. This highly unusual sequence of floods had substantial socio-economic impacts, including a paradigm shift in flood mitigation measures in Europe. A series of volcanic eruptions along with multi-annual Arctic sea ice retreat is a plausible cause of this flood sequence. Clusters of extreme floods occurring within a few months are rarely considered in risk management8. Quick and proactive risk strategies are needed that account for this eventuality.

Understanding the factors influencing soil erosion in agricultural catchments is crucial to reduce land degradation. This study investigated the benefit of disdrometer information beyond rain-gauge observations and the factors that explain the variability in event-scale suspended sediment load. The study seasonally assessed multi-year, high frequency observations of rain-gauge and disdrometer-based rainfall and suspended sediment load measurements in the 66 ha Hydrological Open Air Laboratory agricultural catchment in Austria. Hydrometric and land management information was combined to understand the factors controlling erosion. The results confirmed that the kinetic energy of rainfall and rainfall erosivity estimated by disdrometer and rain gauge data were comparable (r = 0.93 and 0.95, respectively). Rainfall erosivity alone did not fully explain the observed variability in SSL. Winter events with small kinetic energy caused large total event suspended sediment load because bare and saturated soils reinforced surface runoff. In the spring and summer months, the average size and velocity of the drops were the largest and events with larger number of drops in the most frequent velocity class were associated with larger total event suspended sediment load. In spring and summer, the sediment response to a given level of rainfall erosivity was modulated by land management state and antecedent soil moisture rather than by rainfall properties alone. This study demonstrated that instead of disdrometers rain gauge-based estimates can suffice to estimate the erosivity of rainfall events which can be linked to soil erosion. Still, the disdrometer data provided additional knowledge on detailed characteristics of the rainfall events.

Abstract This study explored the potential of using saturated area patterns in understanding tile drain response from an agricultural hillslope. Using spatial analysis of time-lapse imagery gathered from the Hydrological Open-Air Laboratory (HOAL) in Petzenkirchen, Lower Austria, twelve events between 2015 and 2017 have been analyzed. Visual inspection of the analyzed events reveals connected or disconnected saturation patterns in the downslope direction of the hillslope. The connected events show consistently larger average maximum connected distance (> 32 m) and average saturated area (> 50 m 2 ) during the events. The results show that for the connected events, tile drain response is always low, while for the events that become connected, the tile drain response increases rapidly with connectivity distance. Overall, the observed saturated areas are shown to outperform traditional explanatory variables of tile drain response, with the exception of event groundwater levels, with a 59% and 62% of behaviour explained for peak tile drain discharge and event volume, respectively. Camera observations are sensitive to snow cover and surface vegetation, which necessitates a manual pre-screening of the captured events for possible use. Overall, the findings of this study suggest that observations of saturated areas are highly informative for predicting hillslope scale runoff and related hydrological processes because they provide spatially distributed rather than point scale information, which presents a cost-effective method with potential of much wider use in hydrology.

Abstract. Understanding how flood frequency changes under non-stationary hydro-climatic conditions remains a key challenge in hydrology. This study presents a Bayesian process-based framework for flood frequency analysis that explicitly accounts for the seasonal dependence of rainfall–runoff processes and their sensitivity to climate change. The approach links an event-based rainfall–runoff model with probabilistic representations of storm, soil moisture, and catchment response, allowing the joint propagation of uncertainty from climate drivers to flood quantiles. The process-based structure of the framework also enables the disentangling of individual flood drivers, such as the upward shift of the zero-degree isotherm, long-term changes in soil moisture regimes, and variations in precipitation intensity. The framework is implemented in Austrian hotspots, i.e. groups of similar catchments, using long-term hydrometeorological records and regional climate projections (EURO-CORDEX). Results show that (i) changes in flood frequency are primarily driven by projected increases in precipitation intensity, while temperature and soil moisture act as modulators or amplifiers of this signal; (ii) precipitation changes have larger but more uncertain impacts on floods than temperature and soil moisture variations; (iii) the expected reduction in soil moisture tends to mitigate frequent floods but has mores limited influence on rare events. The proposed methodology provides a transferable tool for assessing climate-sensitive flood hazards in non-stationary environments.

Study region: Karst springs draining the Hochschwab massif, Eastern Alps, Austria. Study focus: Accurate forecasting of spring discharge and water quality is crucial for sustainable water resource management. Although machine learning (ML) models have shown considerable potential for forecasting hydrological variables, understanding the underlying processes remains limited. This study aimed to improve the transparency of ML models through an attribution analysis, which explores the contribution of local environmental factors to forecasts. Several ML models were deployed to predict spring discharge and water quality, measured by the spectral absorption coefficient at 254 nm (UV254), up to four days in advance at karst springs. Innovative insights: The Deep SHAP method aided in identifying significant seasonal variations in model attributions, showing the most pronounced changes for snow depth, followed by physicochemical variables such as electrical conductivity and other meteorological variables. The Transformer model exhibited the best overall performance. Model uncertainty, assessed through the Deep Ensemble method, is greater in spring and summer, and both the model errors and uncertainties increase with variability of the target variables. To evaluate model applicability for selective water abstraction, we classified UV254 forecasts based on threshold exceedance, achieving high classification accuracy (>95 % for 1-day and >90 % for 2-day forecasts). Integrating Deep SHAP and Deep Ensemble methods enhanced ML transparency. This combined approach provides insights that can inform drinking water management decisions in karst systems.

Water management interventions are designed to mitigate undesirable aspects of coupled human-water systems (CHWS). However, due to the nonlinear feedback mechanisms inherent in CHWS, these interventions sometimes lead to unintended consequences that exacerbate the very issues they aim to resolve. To develop a generalized understanding of the underlying mechanisms behind such unintended outcomes, this study conducts a meta-analysis of 37 case studies from around the world. We identified six core subsystems and defined a critical pathway showing how hydrological perturbations propagate in a CHWS and lead to unintended consequences of interventions. By analysing case storylines, we identified the critical pathways and harmonize them into prevalent critical pathways, which most frequently lead to unintended consequences for specific phenomena, together with the key variables. The results of this study can support more sustainable and resilient water management, as it is the critical pathways that must be altered to avoid unintended consequences.

Saturation excess and infiltration excess are two primary surface runoff generation mechanisms governing the timing and magnitude of streamflow at the catchment and larger scales. Despite their frequent co-occurrence and interconnections within catchments, most existing runoff schemes treat these mechanisms separately, following different theoretical paths. This study addresses this theoretical inconsistency by introducing a unified runoff scheme that integrates both mechanisms into a coherent framework. The scheme mathematically expresses both saturation and infiltration excess as functions of the probabilistic distribution of soil water storage, allowing dynamic transitions between mechanisms both in space and time based on the evolving soil water storage distribution during storm events. To demonstrate the applicability of this scheme, we developed a simple hydrologic model and tested it in 181 natural catchments over the U.S., spanning a range of humid to arid climates, and obtained Kling-Gupta efficiencies above 0.5 for 90 % and 70 % of the catchments during the parameter determination and validation periods, respectively. Results show that the model effectively captures the relative dominance of infiltration or saturation excess runoff at the event, seasonal, and annual scales. For instance, model results suggest that infiltration excess runoff dominates where the climate is arid and seasonal evaporative energy and precipitation are in phase, whilst saturation excess runoff dominates under other climate conditions. This unified scheme establishes a new foundation for enhancing the predictive understanding of runoff and other hydrological processes across diverse climates.

Understanding the discharge response of tile drains is essential to better manage water resources in agricultural areas. The aim of this study was to compare two tile-drainage systems in an agricultural catchment, the Hydrological Open Air Laboratory in Austria in May-October, using six years of high-frequency hydrometric and isotopic observations, to determine whether new or old water contributes more to their discharge and whether new-water contributions correlate with hydrograph flashiness or time to peak flow. The results showed clear differences between the discharge responses and new water fractions of the two tile drainage systems. Sys4 inlet represented a perennial system with larger drainage area, larger pipes and simpler topology, that responded rapidly to even small amounts of rainfall. The response was mainly independent from the soil moisture state and the groundwater level. The second drainage system, Frau2 was an ephemeral system with smaller drainage area, smaller pipes and complex topology that exhibited the flashiest behavior. Larger discharge peaks at Frau2 occurred above a certain soil moisture (0.35 m3/m3) and groundwater level threshold (0.3 m below ground surface). Sys4 had the largest average peak flow new water fractions (0.54 with delta 18O), higher than Frau2 (0.47) and the catchment outlet (0.41). The differences in the hydrometric and isotopic characteristics of the drains could be explained by differences in their construction properties. Discharge from the drainage system draining larger area and with simpler topology and pipes with larger diameter had faster response to rainfall and larger new water fractions.

When vegetation intercepts precipitation, the quantity of rainwater reaching the ground is affected, as it passes through the canopy, drips from it, and runs down the stem. Interception also significantly alters the characteristics of rainfall, which is among others reflected in differences in the number, size and velocity of raindrops. Throughfall drop size distribution was monitored and analysed for three vegetation types, including a single pine tree in an urban park, trees in an urban mixed forest, and a maize field in an agricultural area. Velocity-diameter diagrams were compiled for the 33 selected throughfall events and grouped into three distinct clusters based on similarity using a hierarchical clustering approach. Pine throughfall events were grouped in Cluster 1, urban mixed forest events in Cluster 2, while maize events were split between Clusters 1 (with all the pine tree events) and Cluster 3. A detailed analysis of rainfall microstructure characteristics under maize and pine canopies was conducted in relation to the rainfall event conditions and crop growing stage to evaluate why, in some cases, throughfall microstructure under maize is similar to that beneath pine (events assigned to Cluster 1), and, in other cases, it differs (events assigned to Cluster 3). Throughfall events in Cluster 3 were generally larger and more intense, showing a unimodal temporal distribution. In contrast, maize throughfall events in Cluster 1 exhibited a bimodal distribution, with two intensity peaks separated by a rainfall break. Notably, the maize leaf area index (LAI) exceeded a value of 4 during the period when the shift occurred from the events assigned in Cluster 1 to the subsequent events assigned in Cluster 3. As maize leaves mature, they become less flexible and do not bend as much under the weight of rain. Consequently, throughfall consist of more drips (larger drops) than direct rainfall (smaller drops). Further research could include additional types of vegetation, and the results could be supported by measurements over a longer period of time. These values could also be used for direct analyses of rainfall erosivity.Acknowledgment: This contribution is part of the ongoing research project entitled “Evaluation of the impact of rainfall interception on soil erosion” supported by the Slovenian Research and Innovation Agency (J2-4489) and the Austrian Science Fund (FWF) I 6254-N.

Compound spatial precipitation events, occurring when extreme or moderate precipitation values manifest simultaneously or in sequence across multiple regions, amplify hydrological risks far beyond those of isolated events. This study assesses, at global scale, changes in compound spatial precipitation from 1980 to 2024, enabling the disentanglement of the individual contributions of spatial extent and intensity across regions. Our findings reveal that the expansion rate of the concurrent spatial area generally outpaces its intensification rate globally. This divergence is particularly pronounced in the tropical zone, suggesting that enhanced moisture supply in a warming atmosphere may be driving the increased spatial organization of extremes.

To enhance hydrologic modeling, the hydrology community has developed benchmark datasets (e.g. Model Parameter Estimation Experiment, MOPEX), providing standardized data for model evaluation and parameter estimation. However, these datasets primarily focus on modeling natural hydrologic processes, leaving a critical gap in understanding the role of human influences. Here, we introduce the Coupled Hydrology-Human Activity Information (CHHAI) dataset, a benchmark dataset that integrates coupled human-water data from regions across all continents, excluding Antarctica. CHHAI incorporates data from 25 regions that cover various human impacts such as reservoir management, flood protection, river management policies, land use changes, and water use. Each basin reflects distinct challenges, providing a diverse and globally representative resource for researchers studying these processes. By offering standardized datasets for modeling and analysis, CHHAI aims to enhance our understanding of interactions between people and water and to support the development of improved strategies for managing coupled human-water systems.

Karstquellen liefern Trinkwasser für etwa 10

The future of hydrology lies in learning from patterns to understand processes across scales.

Preferential flow paths (e.g., macropores or subsurface pipe networks) in hydrological systems facilitate the rapid transmission of precipitation and solutes to streams, resulting in streamflow responses characterized by the release of younger water (i.e., recent precipitation) from the catchment and correspondingly short transit times (on the order of days). While preferential flow paths are documented in both the unsaturated zone and groundwater aquifers, it remains uncertain whether catchment-scale isotope-based transport models can adequately represent preferential flow using tracer measurements in streamflow. In this study, we hypothesize that the preferential release of young water from both the unsaturated zone and groundwater aquifers can be isolated from the streamflow tracer signal. This can be studied with StorAge Selection (SAS) functions, which describe how young or old water leaves a storage. We systematically compared multiple parameterizations of SAS functions describing how water of different ages is released from the unsaturated zone and groundwater aquifer within a single catchment-scale transport model using long-term measurements of hydrogen isotopes in water (delta H-2) from two headwater catchments (the Hydrological Open Air Laboratory (HOAL) in Austria and the Wustebach catchment in Germany). The results show that delta H-2 measurements in streamflow exhibited sufficient variability to isolate the preferential release of younger water through preferential flow paths in the unsaturated zone. In contrast, the variability of delta H-2 in streamflow was insufficient to isolate the preferential release of younger water from the groundwater aquifer, as any seasonal variations in pore water delta H-2 were largely damped by substantial passive groundwater storage (water that mixes with the tracer signal of the active groundwater volume). Consistent with this interpretation, the degree of attenuation in the simulated streamflow isotope signal increased with increasing passive groundwater storage volumes and became pronounced when passive storage was orders of magnitude larger than active groundwater storage. The size of passive groundwater storage, in combination with groundwater SAS function parametrizations, regulated the long tails 100

River floods are among the most disastrous and costly extreme weather events around the world. Atmospheric blocking events (persistent, slow-propagating and self-preserved weather systems that 'get stuck' and 'slow down' the atmospheric circulation) are a key feature of the persistent weather regimes over the Euro-Atlantic, playing a central role in shaping the weather and extreme weather of Europe. Notwithstanding this socioeconomic importance, the covariability between atmospheric blocking and river flooding has rarely been examined at the climate and continental scales. Our study explores the hydrological way in which atmospheric blocking propagates into river floods, and how this relationship varies across space and time. We analyse observed flood discharge across the continent together with atmospheric and terrestrial variables derived from reanalysis, resulting in >6000 rivers analysed over the last 60 years. We combine process-based hydrological analysis and climate analytics to obtain the influence of atmospheric blocking on floods in Europe across spatial and temporal scales, characterising the seasonal behavior, temporal dynamics over decades, and spatial patterns of this relationship. Our results clearly demonstrate the large-scale signature of blocking on flood behavior, and how atmospheric blocking has influenced the hydrometeorological drivers of river floods at the regional and continental scales, inducing robust patterns of variability across different flood attributes over the last 60 years. Despite strong decadal variability in the hydrological propagation of atmospheric blocking and blocking activity itself, our analyses reveal significantly changing impacts of atmospheric blocking on floods in Europe. Furthermore, our results demonstrate how the effects of atmospheric blocking interact locally with the regional hydrological characteristics. Overall, our findings link a dynamical mechanism with flood behavior within the climate system, improving the understanding of multiple features of flood variability in Europe over the last 60 years.

Reliable quantification of global water-cycle components, such as river flow and land evapotranspiration, remains a major challenge. Here we refine estimates of global water partitioning by combining outputs from multiple Earth system models with river flow observations from 50 large basins, applying the emergent constraint approach. Between 1980 and 2014, global river flow was (39.1 ± 5.4) × 103 km3 yr−1, with a river flow-to-precipitation ratio of 0.35 ± 0.03, both lower than previous estimates. Land evapotranspiration reached (73.4 ± 6.2) × 103 km3 yr−1. Under climate change, we project global river flow to rise by 7.8 ± 5.5 mm per year per degree of warming. This estimate, refined through the emergent constraint method, is 9.3

Abstract. Sociohydrology has advanced explanations of coupled human-water systems, but its translation into decision support remains uneven. We argue that a practical bridge from systems thinking to systems doing can be built around two complementary abstractions: (i) canonical feedback structures that make cases comparable without erasing context, and (ii) critical pathways that trace how interventions propagate through behavior, exposure, and outcomes. Canonical forms help organize recurring emergent phenomena such as the levee effect and reservoir effect, while critical pathways turn these insights into a repeatable workflow for intervention design, monitoring, and adaptive learning. We propose a minimal "systems doing loop" that links feedback mapping, pathway tracing, indicator selection, and iteration, with equity and legitimacy treated as explicit constraints on what counts as useful knowledge and acceptable action. This framing complements integrated water resources management by making unintended consequences operational and by clarifying what to monitor and revisit when system behavior changes.

Different types of vegetation shape throughfall in various ways, which affects erosion and runoff generation processes. Direct comparisons of throughfall drop size distributions (TF DSD) across different vegetation systems remain unexplored; therefore, this study quantifies TF DSD of 33 events under three distinct vegetation types (a pine tree, a mixed forest, and a maize field) within a unified framework. The amount and duration of TF was the highest in the forest, while TF under the maize exhibited the largest and the most uniform intensity regime. The diameter of the drops under the maize was the largest, resulting also in the highest proportion of drips (drops > 2 mm). In the forest, the proportion of drips remained constant between events, while under the pine tree the splashes prevailed (drops < 1 mm). Further analysis of the TF DSD with velocity-diameter diagrams, grouped according to their visual similarity with hierarchical clustering, resulted in three distinct clusters (C1-C3). Events under the pine were placed in C1 and those under the mixed forest in C2, while events under the maize were divided between C1 and C3. In comparison with C1, C3 events were characterised by increased TF intensity and larger TF drops. This indicates that there is a dynamic control of TF DSD under the maize, dependent on the interaction between the characteristics of rainfall and the canopy. Understanding these differences can support the design of vegetation cover in agricultural and urban landscapes to enhance infiltration, reduce soil erosion, and regulate runoff.