The Atlantic Meridional Overturning Circulation (AMOC) moderates the European climate. The AMOC is a tipping element and may collapse to a substantially weaker state under climate change. Such an event induces global and regional climate shifts. The European hydroclimate becomes drier under an AMOC collapse, this response is not considered in the standard hydroclimate projections. Our results indicate a considerable influence of the AMOC on the European hydroclimate.

Green water stress can negatively affect agricultural production and is often mitigated through irrigation. In this global modelling study, we investigate where and to what extent the implementation of irrigation helps to compensate for green water stress but at the same time leads to an increase in blue water scarcity. Our findings highlight the need to consider both water stresses together, along with their dynamic interactions, for sustainable water management.

Our study evaluated 24 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP (Multi-Source Weighted-Ensemble Precipitation) V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GDAS (Global Data Assimilation System), demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.

Trees in rocky landscapes often depend on hidden water deep underground, yet the role of bedrock cracks remains unclear. In a karst forest in southwest China, we traced water movement and seasonal use by trees using stable isotopes. We found that large soil-filled fractures can store water for months, providing a crucial supply in the early growing season and helping forests cope with wet–dry changes in rainfall patterns.

The influence of climate change on drought in the UK has gained attention recently. However, a probabilistic assessment of temperature’s nonstationary influences on hydrological drought characteristics, which could provide key insights into future risks and uncertainties, has not been conducted. This study evaluates changes across seasons and warming scenarios, finding that rare droughts may become more severe, while frequent summer droughts are shorter but more intense.

Arctic rivers move enormous amounts of water and carbon into the ocean, influencing global climate, but their flow is hard to predict because the region is remote and the frozen ground behaves in unusual ways. This research combines artificial intelligence with the physics of snow and permafrost to forecast river flow more accurately. Demonstrated on the Kolyma River, the new model outperforms existing approaches and provides a robust framework for understanding Arctic hydrological systems.

As glaciers retreat in mountain regions, heavy rainstorms increasingly control how much soil and sediment rivers carry downstream. We analysed rainfall and sediment data over 21 years in the Austrian Alps and found heavy precipitation events becoming more important for sediment movement, although total annual suspended sediment transport is declining as glaciers shrink. This shift may increase flood hazards, affecting ecosystems and water quality downstream.

The GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On satellites monitor changes in Earth's water storage by observing gravity variations. By integrating these observations into hydrological models through data assimilation, estimates of groundwater, soil moisture, and hydrological trends are improved, helping to monitor droughts, floods, and human water use. This review highlights recent advances in GRACE data assimilation, identifies key challenges, and discusses future directions with upcoming satellite missions.

Observations and reanalyses show widespread declines in annual precipitation but increases in one-day maxima in Southern Europe. Very high-resolution simulations over a representative Mediterranean sub-region isolate sea surface temperature effects: with all else unchanged, projected warmer seas mainly raise the frequency of heavy rainfall over land by intensifying previously moderate events. For the most extreme cases, mean overland totals change little because peak rainfall remains offshore.

This study introduces a new interpretable deep learning method that accurately predicts multi-depth soil moisture simultaneously without physical assumptions. The model provides insights into soil properties, while delivering precise predictions across diverse scenarios. Tested under various conditions, it outperforms traditional approaches, particularly when enhanced with basic physics. This tool can help improve water management by offering reliable and efficient soil moisture forecasts.

The Aral Sea is both an example of large-scale environmental degradation caused by human activity and a message of hope through its partial restoration. Our study shows that the restored part of the Aral Sea is now healthy in terms of vertical mixing and oxygenation. However, small perturbations of water level or transparency could significantly alter the entire ecosystem. The results contribute to understanding the consequences of large-scale lake management worldwide.

The analysis of nutrient dynamics during precipitation events using normalised cumulative loads (NCL) is a classic and frequently used method of water quality analysis. Our approach combines the classical pollutograph with hydrograph separation using stable water isotopes. The pollutograph is set up for each of the separated runoff components (pre-event water and event water) and the export dynamics of the runoff components of an event are compared.

Throughfall in forests varies in space and time creating distinct patterns. We developed a novel throughfall monitoring approach for continuous, automated measurement that features 60 self-built and cost effective throughfall samplers. Collected data show the potential of the approach to capture throughfall variability at small distances, among and within rainfall events and between different trees species.

Plants typically transpire less with rising atmospheric carbon dioxide, leaving more water in the ground for human use, but many future water scarcity assessments ignore this effect. We use a land surface model to examine how plant responses to carbon dioxide and climate change affect future water scarcity. Our results suggest that including these plant responses increases overall water availability for most people, highlighting the importance of their inclusion in future water scarcity studies.

Our study assesses water stress in Chile from the mid-20th century to the end of the 21st century using novel datasets on water availability, land use, and water demand. We compute a water stress index for all basins in the country and show that, in addition to declining precipitation, rising water demand drives a steady increase in stress. As a drier future is projected for central Chile, the water stress index provides a useful tool for guiding water governance and adaptation strategies.

Explore2 provides hydrological projections for 1,735 French catchments. Using QUALYPSO (Quasi-Ergodic Analysis of Climate Projections Using Data Augmentation), this study assesses uncertainties, including internal variability. By the end of the century, low flows are projected to decline in southern France under high emissions, while other indicators remain uncertain. Emission scenarios and regional climate models are key uncertainty sources. Internal variability is often as large as climate-driven changes.

Iceland relies heavily on hydropower, making it important to understand changes in streamflow. Rivers reflect a warming, wetter climate, with rainfall increasingly replacing snowfall. Annual streamflow has generally increased, most notably during the cool season, while summer flows have declined in some non-glacial rivers. Glacial rivers show increasing melt-season flows over 1973–2023 but declining trends over 1993–2023, reflecting glacier retreat and recent North Atlantic sea surface cooling.

Using global and regional datasets, we compared attention-based models and Long Short-Term Memory (LSTM) models to predict hydrologic variables. Our results show LSTM models perform better in simpler tasks, whereas attention-based models perform better in complex scenarios, offering insights for improved water resource management.

Continuous and high-quality meteorological datasets are crucial to study extreme hydro-climatic events. We here conduct a comprehensive spatio-temporal evaluation of precipitation and temperature for four climate reanalysis datasets, focusing on mean and extreme metrics, variability, trends, and the representation of droughts and floods over Switzerland. Our analysis shows that all datasets have some merit when limitations are considered, and that one dataset performs better than the others.

This study presents a cost-effective method to reconstruct lake bathymetry using only digital elevation model (DEM) data, without relying on field measurements or optical imagery. By simulating lake-level decline and using shoreline topography, it accurately infers underwater terrain. Validation across 12 lakes on the Tibetan Plateau and Lake Mead demonstrates good accuracy and generalizability, enabling scalable lake storage estimation for hydrological and ecological applications.

This study assesses how spatial patterns, temporal trends, and record length in hourly precipitation data affect annual maximum intensities estimated with stationary and non-stationary models across a climatically and topographically diverse region. Comparing five gridded datasets, we find consistent spatial patterns but notable differences in intensities, with non-stationary estimates generally slightly lower.

When floods happen during or shortly after droughts, the impacts of each of the events can be magnified. In hydrological research, defining these events represents a challenging and important task in the process of understanding where and why they occur. We have used real-word examples to address some of these challenges and show different approaches influence outcomes. We make suggestions on when to use which approach and outline some pitfalls of which researchers should be aware.

Our paper introduces Saudi Rainfall (SaRa), a high-resolution, near-real-time rainfall product for the Arabian Peninsula. Using machine learning, SaRa combines multiple satellite and (re)analysis datasets with static predictors, outperforming existing products in the region. With the fast development and continuing growth in water demand over this region, SaRa could help to address water challenges and support resource management.

An ecosystem services model was applied across Germany to estimate water supply and carbon sequestration. The results showed that total annual water discharge and carbon sequestration for Germany is 85 billion m³ and 106 Tg C, respectively. Furthermore, we found that croplands provide the largest amount of water, deciduous broadleaf forests sequester most of the carbon, and wetlands are very effective in absorbing carbon. During extreme events, we noticed a real impact on both services.

This technical note evaluates the efficacy of the Self-Organizing Map (SOM) algorithm to represent concentration-discharge hysteresis patterns in watersheds. Through a proof-of-concept with sediment hysteresis, we show that the SOM algorithm accurately characterizes loop shape and can also be used to reveal the controls of hysteresis patterns in watersheds. We developed a Python package along with this technical note to support broader adoption of the SOM algorithm for hysteresis workflows.

The development of carbon processes in the water quality model WSFS (Watershed Simulation and Forecasting System)-Vemala presents a significant advancement in simulating both total organic and inorganic carbon dynamics, burial and emissions through a river/lake network. The addition of organic acids to the total alkalinity definition improved pH simulations and thus the simulation of CO₂ emissions in the acidic and organic rich waters of Finland. The new Vemala model provides a robust foundation to support water management in the future.

To improve hydrological uncertainty estimation, recent studies have explored machine learning (ML)-based post-processing approaches. Among these, quantile random forests (QRF) are increasingly used for their balance between interpretability and performance. We develop a hydrologically informed QRF trained in a multi-site setting. Our results show that the regional QRF approach is beneficial, particularly in catchments where local information is insufficient.

Water resources face more challenges from climate change and human activities. We improved global water modeling by developing a high-resolution system using Soil and Water Assessment Tool (SWAT+), using automated reproducible workflow. This approach simplifies tracking the progress of global impact assessment modelling efforts. The global model will further help assess water stress hot-spots and inform sustainable water management as further improvements come.

Turbidity is a key factor for water quality monitoring. Here, an image-based procedure is tested in a full-scale river monitoring experiment using digital cameras. This approach can enhance our understanding of the real-time status of waterbodies, overcoming the spatial and temporal resolution limitations of existing methods. It also facilitates early-warning systems, advances water research through increased data availability and reduces operating costs.

This study presents a method to make the spatial resolution of global Water Storage Compartments (WSCs) compatible with terrestrial water storage (TWS) data from Gravity Recovery And Climate Experiment (GRACE) missions. The method compares the spatial structure of the WSCs and TWS by considering the correlation between neighboring grid cells. An isotropic Gaussian filter with an optimal filter width of 250 km is found to be the most suitable, ensuring compatibility for consistent comparison with GRACE data in hydrological applications.

Understanding and modeling flash-flood-prone areas remains challenging due to limited data and scale-relevant hydrological theory. While machine learning shows promise, its integration with process-based models is difficult. We present an approach incorporating machine learning into a high-resolution hydrological model to correct internal fluxes and transfer parameters between watersheds. Results show improved accuracy, advancing the development of learnable and interpretable process-based models.

This study focuses on hyperdroughts (HDs) in central Chile, defined as years with a regional rainfall deficit exceeding 75 %. Only five HDs occurred in the last century (1924, 1968, 1998, 2019, 2021), but they caused disproportionate environmental and social impacts. In some systems, the effects were larger than expected from those considering moderate droughts and dependent on the antecedent conditions. HDs have analogs from the remote past, and they are expected to increase in the near future.

Belgium has faced intense droughts in recent years, causing major losses across sectors. To assess their rarity, we used a hydrological model to reconstruct fifty years of soil moisture in the country. We show that 2011–2020 experienced the most severe droughts since 1971, with nearly 30% of the decade under drought. We also show that rainfall-based indicators underestimate soil moisture droughts, so including soil-moisture monitoring can give decision-makers a clearer picture of drought risks.

We improved the widely used SWAT (Soil and Water Assessment Tool) model to better predict how pesticides move through the environment. We added a new process that considers how plants take-up chemicals from the soil. Testing this updated model in two catchments showed very good prediction capabilities and a reduction of chemicals in river water by up to 17 % due to the plant uptake. The enhanced model offers a valuable tool for assessing the environmental impacts of agricultural management.

We studied an arid, endorheic basin in the Chilean Altiplano to understand how rainfall and evaporation affect groundwater and water availability. Using a rainfall-runoff model and 40 years of satellite data, we found that much of the water evaporates and less reaches the aquifers than expected. Our results challenge the idea that the basin is fully closed and suggest that current water budget estimates may need revision, an urgent task under a changing climate.

This study focused on the sources of the Zambezi River, revealing new insights into its longest tributary and Angola's key role in its flow. River expeditions and earth observation data show that Angola contributed approximately 73 % of the river's flow upstream of a major floodplain during the 2023 late wet season. We highlight Angola's wetlands, which support river health and help mitigate pollution. These findings stress the need for improved monitoring and regional cooperation in the basin.

Automatic water samplers help to collect water for isotope analysis, but if the samples are stored in open bottles for long periods of time, their composition can change due to evaporation. We have tested various barriers to prevent this and found that the tube-dip-in method as an evaporation barrier is the most practical solution. Our results highlight the importance of bottle design and handling, especially for small sample volumes, to ensure accurate water analysis.

Forests can change the kinetic energy of rain below them. We built a new model that breaks down the canopy into layers, and tracks two types of raindrop: direct splashes and water dripping from leaves. The model was validated through nine rainfall events. The canopy doesn't always reduce the rain's force, and sometimes it increases it, depending on the specific structure of the leaves and branches.

We developed a literature-based approach for projecting future snow depths, which was applied to four measurement stations in Switzerland under a +2 °C temperature scenario, revealing significant declines in snow depths. Validation against published data shows that the approach captures key trends in snow loss. This resource-efficient method provides a practical tool for estimating climate change related snow depth declines, which are lacking highly resolved climate projections.

LSTM (Long Short-Term Memory) networks are excellent deep-learning tools to reproduce stream temperature observations, but their performances over the range of extreme (summer) stream temperature values have been overlooked. We tested strategies to improve the LSTM performances over the highest 10 % values of stream temperature observations. We found that the best strategy is to train the LSTM models at several locations with input variables that include static, catchment and reach attributes.

A five-year study of hot springs at a fault intersection on the southeastern Tibetan Plateau developed an anomaly detection model that links synchronous changes in water chemistry to earthquakes with magnitude ≥4. The model combines multiple components to improve accuracy of earthquake timing forecasting and identify reliable predictors. Stronger or closer earthquakes show more components with synchronous anomalies, providing a valuable reference for real-time forecasting in high-risk areas.

This study evaluates the extrapolation performance of long short-term memory (LSTM) networks in rainfall–runoff modeling, specifically under extreme precipitation conditions. The findings reveal that the LSTM cannot predict discharge values beyond a theoretical limit and that this limit is well below the extremity of its training data. This behavior results from the LSTM's gating structures rather than saturation of the cell states alone.

Climate change impacts on a hydrologic phenomenon termed the mid-summer drought (MSD), affecting water and food security of smallholder farmers in Central America, depends on how the MSD was defined. We used many climate model projections and MSD definitions to quantify the sources of uncertainty in future MSD characteristics in Nicaragua for varying levels of global warming. We found that the definition can change where the MSD occurs but it does not add substantially to the total uncertainty.

The review highlights the need to integrate urban land surface and hydrological models to better predict and manage compound climate events in cities. We find that inadequate representation of water surfaces, hydraulic systems and detailed building representations are key areas for improvement in future models. Coupled models show promise but face challenges at regional and neighbourhood scales. Interdisciplinary communication is crucial to enhance urban hydrometeorological simulations.

Intermittent streams are vital to ecosystems and water supply, but are hard to monitor and increasingly affected by climate change. To address this, we used field camera images from 2017 to 2023 at a stream in northern California to train a machine learning model that classifies streamflow as dry, low, or high. This low-cost method enables monitoring of changing intermittent stream conditions and supports water management in data-scarce regions.

We present a neural network model that estimates river discharge based on gridded elevation, precipitation, and solar radiation. Some instances of our model produce more accurate forecasts than the European Flood Awareness System (EFAS) when simulating discharge with lead times of 50 days on the Neckar river network in Germany. It consists of multiple components that are designed to model distinct sub-processes. We show that this makes the model behave in a more physically realistic way.

This study provides the first assessment of an European meteorological dataset (E-OBS) for hydrological applications. We compared the dataset to meteorological datasets developed at a country level, and tested how the different data influenced the simulation of streamflow with hydrological model. Our findings show that, despite some limitations, the European dataset offers a reasonable basis for hydrological modelling in most river catchments across Europe.

We present a new strategy to calibrate large-domain land/hydrology models over diverse regions. Using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models, our approach integrates catchment attributes, parameters, and performance metrics to optimize streamflow simulations. Leveraging advances in machine learning for hydrology, we improve calibration and enable regionalization to ungauged basins, which is valuable for national-scale water security studies.

Climate change, rising [CO₂], and vegetation dynamics are reshaping global water cycle, but their impacts remain unclear. We improved the coupled carbon and water model to analyze China’s water yield (WY) changes (1982–2017). Our results showed that climate change was the dominant driver nationally, vegetation/ [CO₂] most affected in 400-1600 mm precipitation zones. Projections indicate [CO₂] may outweigh vegetation effects on WY by 2100. This work informs sustainable water management.

Abandoned mine systems release contaminants through episodic connectivity rather than steady seepage. At the Reiche Zeche mine, we show that low flow and pre-flush phases accumulate solutes that are rapidly exported during short-lived reconnection events. These hot moments dominate annual metal loads, highlighting the need for event-sensitive monitoring and targeted, near-source remediation strategies.