We developed a new method to improve numerical models that predict future water availability under climate change. The method works across different regions and climate scenarios. Applying the method to mountain river basins in the Eastern Mediterranean island of Cyprus showed that streamflow could drop by 39 % on average between 2030 and 2060, and by up to 70 % during the driest years. These findings help support better water planning in a changing climate.

To better predict mountain hazards like landslides, we studied how long soil retains rain moisture. Using 20 years of satellite data from China, we found a control shift at about five years. Short-term memory is governed by weather and plants, while long-term persistence is locked in by soil and terrain. This creates a lasting "background" wetness, especially in humid forests, pre-conditioning slopes for years.

A variety of real-world applications require estimates of historical weather from anywhere on Earth. The best available data products, such as ERA5, often capture large-scale weather patterns well, but struggle to capture local weather behaviour. We propose a simple and fast statistical method that takes in ERA5 weather and outputs improved simulations of local weather. The method is shown to improve local representations of historical daily temperature and precipitation all over Europe.

Small farm reservoirs are infrastructures for storing water that farmers can use to irrigate their crops, and thereby secure or enhance food production. These are found in many regions of the world. However, small reservoirs can modify flow regimes as they store water derived or extracted from the stream. In this study, we use a modeling approach to evaluate how flows are influenced by the number, capacity, and distribution along the stream of small reservoirs.

This study explores how nanoplastics threaten the freshwater beneath small islands, which is vital for local communities. Conventional water management targets saltwater intrusion, but oceanic nanoplastics spread faster and wider, reaching wells sooner. Salt-based safety standards thus grossly underestimate nanoplastic risks. To protect island water, we must now consider this emerging plastic pollution when placing wells and planning pumping.

The North China Plain, home to over 300 million people, faces groundwater decline from irrigation, creating 80-meter-deep zones threatening supplies. We modeled rain and river infiltration using borehole and weather records to study restoration. Rain recharge averages 446 days, varying nearly 140 times: 10 days in sandy western foothills to 1395 days in clayey central/eastern plains. River recharge is faster at 91 days, suggesting flood basins could accelerate recovery for sustainable farming.

Cattle ponds are commonly used for cooling livestock and for irrigation. Levels of bacteria Escherichia coli in water characterize water quality. We developed the model of fate and transport of E. coli using the HydroGeoshere modeling platform. Pond surface was imaged to estimate the E. coli load to the pond from animals in water. This work shows the opportunity and the approach to obtaining a moderately accurate forecast of microbial water quality in cattle ponds using readily available data.

We used a modelling approach supported by stable water isotopes to explore how forest management – such as conifer, broadleaf, and mixed tree–crop systems – affects water partitioning and drought resilience in a drought-sensitive region of Germany. By representing forest type, forest density, and rooting depth, the model helps quantify and show how land use choices affect water availability and ecohydrological resilience, thereby supporting improved land and water management decisions.

Our study in the Tarim River Basin tackles the tough balance between farming and nature in dry areas. Using a model, we found that boosting farm income often means less water for ecosystems and more pollution. The solution requires different water use strategies across the region. Importantly, farmland area and crop choices must change with the weather, shrinking in dry years. This approach provides a practical plan to manage water, ecology, and agriculture together.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.