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.

Intensity-Duration-Frequency (IDF) curves is important for designing infrastructure that can withstand floods. We compared traditional interpolation methods with machine learning to map these curves across mainland China. ML using widely available daily gridded data can estimate sub-daily intensity as accurately as methods needing rarer hourly site data. This study provides a valuable understanding for IDF in data-limited regions and generates a new IDF dataset for mainland China.

Traditional precipitation analyses often misrepresent intense rainfall's spatial variability. This study evaluates different spatial covariances to capture this variability in a geostatistical framework. The best covariance includes anisotropy derived from daily climate model simulations, offering a reliable alternative to anisotropy estimation using rain gauges. These findings highlight the importance of including anisotropy when generating precipitation inputs for hydrological 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.

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.

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.

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.

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 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.

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.

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.

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 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. 

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.

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.

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.

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.

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.

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.