We analysed 20 years of satellite data to study changes in surface phytoplankton concentration in Lake Tanganyika. Results reveal strong seasonal patterns linked to known hydrodynamics, with decreasing levels in deep areas and increases in shallow regions. These changes could alter the lake’s ecosystem and threaten the resources that millions of people rely on.

This research investigates how water flows through porous media, focusing on tiny swirling currents called eddies that form around particles. Using experiments and computer, we found that faster flow and larger particles create more eddies, which trap pollutants and slow down their cleanup. A new method is developed to measure these eddies and showed how they can be accurately represented in standard transport models. This study provides a clearer physical basis for predicting pollutant spread.

Two models, AquaCrop (crop model) and Noah-MP (land surface model), were compared estimating irrigation in Italy's Po Valley. Noah-MP simulated higher water use (434 mm/yr) than AquaCrop (268 mm/yr), mainly due to extra water losses like runoff. Once losses were accounted for, both aligned with basin-scale reports of around 500 to 600 mm/yr. The study highlights how complex irrigation modeling is, and the need for better observational data to validate results.

Investigating changing snow in response to global warming can be done with a simple model and only temperature and precipitation data, simplifying snow dynamics with assumptions and parameters. We provide a large-scale and long-term evaluation of this approach and its performance across diverse climates. Temperature thresholds are more robust over cold climates but melt parameters are more robust over warmer climates with deep snow. The model performs well across climates despite its simplicity.

Traditional analysis of extremes often relies on complex tools when the statistics change over time. We developed a novel, efficient framework based on a maximum return period change over a specific timeframe. Simple formulas are derived to determine the variation in average frequency and magnitude of events. The approach has minor approximations compared to more complex methods, thus providing a reliable tool for practitioners to forecast risk assessment under changing environmental conditions.

Land surface models simulate exchanges at the Earth's surface. Traditional evaluation methods can be misleading because they may hide errors or be overly sensitive to outliers. We introduce the Model Fidelity Metric, which measures model performance in terms of accuracy, variability and distribution similarity. Tests with synthetic data and real streamflow observations show that this metric provides more stable and informative assessments of model performance.

This study explores how mean and extreme river flows respond to annual climate variability. Maps show where river flow is more sensitive to climate in Europe. Maximum flows are generally the most sensitive and minimum flows the least sensitive to precipitation changes. Sensitivities are influenced by many factors like climate, soil, and terrain. These findings improve our understanding of how rivers respond to climate and can support water management and disaster risk reduction across Europe.

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.

This study evaluates the skill of National Oceanic and Atmospheric Administration (NOAA) seasonal precipitation forecasts for two west-central Florida river basins. To develop operational precipitation forecasts for hydrologic modeling, two non-parametric methods are examined to sample from pre-generated rainfall realizations to convert probabilistic outlooks into quantitative rainfall ensembles. This study offers a practical pathway to improve hydrologic simulation inputs for water-resources management.

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.

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.

Using reconstructed 3D basin-scale sedimentary architectures, this study quantifies how hierarchical heterogeneity controls solute dispersion. Results show that macroform-scale lithofacies geometry and connectivity dominate macro-dispersion. Basin systems display a prolonged pre-asymptotic and strongly non-ergodic regime, with a buffering effect that reduces realization variability. The study establishes a transferable framework linking hierarchical architecture to multi-scale dispersion.

Understanding drought propagation is vital for disaster preparedness and risk management. This study presents a comprehensive analysis of various drought conditions across global land areas. Interpretable machine learning technique is employed to identify the key factors influencing drought propagation. Results reveal large-scale propagation pathways of meteorological-runoff-agricultural droughts, and highlight how climatic characteristics affect these dynamics.

Multivariate calibration has become a widely used method to improve model realism. We found that multivariate calibration can lead to less constrained flux maps and more uncertain hydrographs relative to univariate calibration. These symptoms could be caused by non-overlapping behavioural parameter distributions for the individual calibration variables. The results emphasize that the value of non-discharge data in calibration is contingent on the suitability of the model structure.

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.

Groundwater is a highly-used water source, which drought management is complicated. We introduce a socio-hydrological water resource model (SHOWER) to aid drought management in groundwater-rich managed environments. Results show which and when drought management interventions influence surface water and groundwater storage, with integrated interventions having most effect on reducing droughts. This encourages further exploration to reduce water shortages and improve future drought resilience.

Stable isotopes of oxygen (O¹⁶/O¹⁸) are powerful tools in eco-hydrology. Yet, a limitation is that the influence of atmospheric circulation on precipitation isotope signals is not well known. Here, we mapped isotope signals from different moisture origins with an air mass trajectory model, revealing contrasted contributions to precipitation isotope signals in Western Europe. These insights are key for interpreting isotope signals to assess changes in the water cycle.

Droughts increasingly threaten agriculture. Improving soils to store more water, for example by increasing soil organic carbon, can help. We simulated this in a Swiss catchment and found that more soil carbon slightly increased soil water storage and evapotranspiration, modestly reduced floods, and shortened periods with very little streamflow. However in warmer, drier areas, these periods with little streamflow could sometimes last longer.