To understand how greening affects forest water use, we combined satellite and climate records for China’s forests over the past 30 years. We found that more leaves no longer increase the share of water used by trees as strongly as before. This weakening is faster in planted forests than in natural forests, and dry soils are becoming a stronger limit. The results can help guide forest planting and water management in a warming climate.

We developed a satellite-based irrigation advisory system that operates weekly, helping water providers make informed, science-based decisions. It estimates crop water needs using satellite data combined with rainfall and past irrigation and can also be used to simulate future cropping patterns under policy changes or reduced water supply. Co-developed with stakeholders, it is scalable to other regions with similar water management challenges.

We mapped dominant hydrologic processes across the United States by analyzing observed streamflow dynamics. Using random forest models and interpretable machine learning techniques, we predicted processes in data-scarce regions and identified key drivers such as climate, soil and geology, land cover, topography, and human influence. The resulting maps of dominant processes and their drivers reveal strong regional patterns that guide hydrologic model selection and water resource management.

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.

Convection-permitting regional climate models, such as ICON-CLM at 3 km resolution, have great potential for improved hydroclimatic simulations. The studied model run shows lower bias in air temperature and summer global radiation, as well as in the frequency of wind speed over the Weiße Elster catchment in East Central Germany. Due to a pronounced overestimation of the intensity and frequency of heavy rainfall, however the discharge estimates are skewed, with no apparent added value.

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.

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.

This paper highlights the importance of discharge assimilation (DA) for artificial intelligence (AI)-based operational discharge forecasting. Using two public datasets from France and the USA, simulated discharge from two rainfall-runoff models, and a multilayer perceptron for implementation, we evaluate three DA strategies under both deterministic and probabilistic forecasting approaches. Results show that DA is crucial and that model performance may decrease between the two forecasting cases.

Climate change and human activities jointly shape river droughts, yet their combined impacts remain uncertain. We pair a data-driven river model with scenario-based climate projections to assess future water shortages in China’s Upper Hanjiang River. We also evaluate improved reservoir operating rules. Results show rising risk of prolonged drought, while refined reservoir operations ease short events but cannot offset long-term deficits, informing resilient water-energy planning.

The quality of large-domain hydrologic model simulations is often quantified with so-called accuracy metrics. Here we use simple benchmarks to provide relevant context for these accuracy metrics. Results show that areas where the model cannot beat the benchmarks do not always align with areas where the accuracy metrics are low. This suggests that model improvements are possible in regions that under more typical model evaluation approaches (i.e., without benchmarks) might not be obvious.

Identifying drivers is crucial for process understanding and predictions. In Hydrometeorological systems, many variables are closely related, and common methods often rely on correlation. We describe theoretically distinct methods of discovering cause-effect relations from data. We evaluate them in a large simulated environment. Results show that finding cause-effect relations provides a parsimonious picture and to obtain robust predictions, especially under changing environmental conditions.

Understanding how baseflow contributes to river flow is essential for managing water resources. We studied a widely used method for separating baseflow and found that a key parameter was often estimated too simply. Using symbolic regression and data from 855 catchments, we uncovered new formulas that greatly improve accuracy and reveal how soil, snow, and catchment size jointly influence baseflow estimation.

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.

The Ucayali River is the major Andean conveyor of sediment to the Amazon. By combining field measurements, satellite data and modelling over decades, we show that floodplains trap 36% of this sediment while recycling 22% back into the river during flood recession. During high waters, flooding reduces the river's capacity to carry sand, capturing 14% at peak discharge. These findings show that floodplains act as dynamic regulators of sediment transport in large tropical rivers.

We examined how permanent gully in farmland regulate the transport of runoff and dissolved nitrogen and phosphorus during natural rainfall. Measurements at both the gully head and the outlet showed that the gully facilitates runoff production, yet diluted nutrient concentrations. High-erosivity storms triggered disproportionately large nutrient losses and markedly altered the gully’s contribution. These findings provide insights for improving nutrient management in gully-dominated landscapes.

Streamflow observations contain information about snow, but their potential to constrain seasonal snow mass reconstructions remains underexplored. Using inverse hydrological modeling, we show that streamflow is particularly effective at constraining catchment-aggregated melt rates, but that non-uniqueness in the snow–streamflow relationship and uncertainties in the inverse modeling chain can easily limit inversion performance.

This study investigates soil water dynamics along an elevation gradient and distinguishes individual soil water pools (mobile vs. tightly bound water). Varying persistence of winter-derived soil water was documented, with longer residence times in lowland areas despite the absence of snow cover. A new method for direct extraction of tightly bound soil water, together with a correction procedure, also revealed distinct seasonal behavior of soil water pools, particularly during spring and autumn.

Existing global evapotranspiration algorithms rely on sparse local measurements and each comes with its own strengths and weaknesses. Here, we proposed an ensemble framework that employed a machine learning system to dynamically select the most appropriate algorithm to be used across spatial and temporal scales, thus fully utilizing the distinct strengths of each method. In multi-scale validations, our framework exhibited enhanced extrapolation performance, stability, and interpretability.

Wetlands are crucial environments, increasingly threatened by anthropogenic activities. The present work proposes a simplified model of wetland dynamics 'WetMAT'), useful to understand the state and potential evolution of the system in a multiplicity of conditions (e.g., climate change). Referring to the Doñana wetland (Spain) we aim at using WetMAT to support estimating water needs in such a complex and fragile ecosystem, providing useful insights for water resources planning and management.

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.

We present a global 0.5° × 0.5° daily reconstruction of terrestrial water storage anomalies from 2002–2021, using a novel four-parameter linear recursive model driven only by precipitation and temperature. The model exhibits strong physical interpretability, efficiently quantifies the precipitation-to-storage conversion fraction, and achieves faster parameter convergence. It outperforms existing models in 89 % of basins, with Nash–Sutcliffe efficiency values exceeding 0.7 in 84 basins. 

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.

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.

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.

Alpine karst springs are important for providing year-around baseflow in mountain streams and sustaining fragile aquatic ecosystems. In this study the hydrogeology of a previously unexplored alpine karst system is characterized using diverse methods including the analysis of snowmelt-driven, diel fluctuations of spring discharge and electrical conductivity. The approach developed here will be transferrable to similar alpine karst systems in snow-dominated environments.

Groundwater contamination can be hard to diagnose quickly when sources are hidden underground. We develop a new framework that integrate multiple search chains in two stages: first they scan widely using an evolutionary algorithm, then they cooperate to refine source locations with Tabu Search. Fast surrogate models replace part of the time-consuming simulations. In case studies, this approach identifies source information more accurately and saves substantial computing time.

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.

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.

Surface sealing makes cities vulnerable to flooding caused by heavy rain. Green infrastructure, such as green roofs, can reduce flooding. This modelling study investigates the potential of green infrastructure to reduce runoff, flood water depth and building damage. Bioretention systems turned out to be the most space efficient compared to green roofs and pervious pavement. For larger rain events, more green infrastructure implementation is needed to achieve relevant flood mitigation.

We studied how the melting of snow affects the release of dissolved organic carbon (DOC) in a northern peatland. Using detailed aerial surveys of snow cover, landscape moisture, and continuous water quality measurements revealed that DOC is released as snow cover melts, especially in wetter areas. Our results show how the snowmelt patterns control DOC transport, highlighting the sensitivity of northern peatland ecosystems to climate-driven changes in snow cover.

The spatial resolution of hydrological modeling is a critical factor affecting flood simulation accuracy, particularly in large watersheds with complex characteristics. This study quantifies the impact of spatial resolution on hourly flood simulation and identifies flood characteristics that are sensitive to spatial resolution. The findings provide practical guidance for selecting appropriate spatial resolutions in large-watershed modeling.

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.