Increased runoff due to ice melt in Greenland contributes to sea-level rise and flooding in coastal settlements. We reconstructed glacier runoff in northwestern Greenland in 1950–2023. Long-term modeling shows a recent increase in runoff linked to changes in atmospheric circulation, characterized by enhanced northward transport of heat and moisture. The results highlight a strong influence of atmospheric variability on glacier runoff and its impacts on local communities in northern Greenland.

We study the variations in annual streamflow and explicit their dependence to climate variations, in order to understand their causes and to provide tools for a rapid assessment of the impact of climate change on water resources. By making explicit the dependency of streamflow elasticity to aridity, we are able to propose a regionalized elasticity formula with physically-realistic elasticity coefficients.

We studied how to add site information to deep learning models that predict groundwater levels at many wells at once. Using data from Germany, we compared four simple ways to combine time varying weather with time invariant site characteristics. All methods gave similar average accuracy. Repeating site data at each time step was slightly best but used more computer power. The informativeness of site information mattered more than the method, guiding future model design.

Isotopic and chemical tracers show that precipitation dominates recharge of shallow pore water in engineered gully zones, whereas deeper fissure water is recharged more slowly through percolation. These systems enhance groundwater recharge in dry regions, providing opportunities for sustainable water management.

Water resources will be heavily impacted by climate change in the future, with low flows and water demand satisfaction expected to decline. This study uses an integrated water resources management model to examine future water demand scenarios, revealing that climate change will be the primary driver of changes. While adapting water uses could mitigate negative impacts, this will not be enough to adapt to climate change. The irrigation sector is expected to be the most impacted.

We present ISONEVA, a new isotope-based framework for estimating soil water evaporation that explicitly accounts for dynamic soil water storage and non-evaporative fluxes. Numerical and field validations demonstrate that ISONEVA substantially improves evaporation estimates compared to traditional steady-state and non-steady-state approaches, and provides a robust basis for assessing long-term average evaporation to precipitation (E/P) ratios.

We studied trifluoroacetate (TFA) in a German river catchment. TFA from rain was not permanently retained in soils or plants and can be mobilized  by heavy rain, meaning soils and plants may hold more TFA than rivers or rainfall indicate. In areas with precipitation input only, TFA served as a new tracer for determining actual evapotranspiration. The agricultural area showed higher TFA in surface and groundwater, suggesting that agriculture increases TFA pollution.

Hydrologists depend on accurate long-term precipitation records to assess hydroclimatic change. This study evaluated six high-resolution precipitation datasets for the southeastern United States (1980–2024) and found that most contain artificial shifts caused by changes in gauge networks and data processing. The Daymet–nClimGrid and Daymet–nClimGrid–PRISM LT combinations produced the most temporally stable datasets, while PRISM LT also performed well as an individual product.

This study developed a new method for classifying catchments, combining machine learning techniques with climate and landscape data. By analyzing catchments across China, we identified six climate regions and 35 unique catchment types, each with distinct streamflow patterns. This classification method improves hydrological predictions, especially in areas lacking direct data.

The Three Gorges Dam (TGD)'s impact on downstream groundwater dynamics remains poorly understood. We established monitoring profiles in the first downstream river-lake wetland to spatially quantify groundwater response for the first time. With numerical simulation, how each operation period affects hyporheic exchange is deeply studied. We found TGD suppresses river-aquifer exchange, particularly during dry-season. These findings provide critical insights into dam effects on watershed hydrology.

Changes in the cQ relationship between two climatically different periods reveal shifts in nutrient, total organic carbon, and mineral storage and mobilization processes. Comparing past and recent periods, our findings indicate increased enrichment behaviour for nutrients, driven by altered solute distribution and hydrological connectivity. This intensifies near-surface transport, increasing the likelihood of water quality exceedances and risks to ecosystems and drinking water supplies.

This study improves river flow prediction by combining two types of artificial intelligence models. One model estimates how rainfall becomes runoff in each part of a river basin, while another represents how water moves through the river network. By linking these processes, the approach better captures how water travels across large basins. The results show more accurate streamflow predictions, which can support water management and flood forecasting.

Travel or transit time distributions play a key role in contaminant leaching from the partially saturated zone into groundwater. Here we show that average travel times are of different water isotopes may differ by 5 %–10 %. These difference arise in case of imperfect mixing due to trapping of isotope molecules in bottle necks of very small hydraulic conductivity. Molecules with smaller diffusion coefficient stay there for a longer time.

We studied a lake in an arid region to understand how climate change affects its size and long-term changes. Using forty years of satellite images, we developed an improved method to create a complete monthly record of the lake's area. Our results show that rainfall drives lake growth in spring and summer, while sunlight and evaporation reduce it in autumn and winter. These findings reveal how sensitive arid-region lakes are to climate change and provide guidance for water management.

This study aims to improve prediction and understanding of extreme flood events in near-natural catchments across the United Kingdom. We develop a machine learning framework to assess the contribution of different features to flood magnitude estimation. We find weather patterns are weak predictors and stress the importance of evaluating model performance across and within catchments.

Water scarcity limits development in many dry regions. This study builds a basin-wide integrated model to optimize water use in China’s Yellow River Basin. Results show that water is scarcer and economically more valuable downstream, increasing pressure on human and ecological water needs. Better coordination of reservoirs and groundwater and shifting water to higher-value uses can improve reliability and productivity. These findings offer lessons for managing scarce water in basins worldwide.

Frequent, low-intensity urban pluvial flooding is notoriously difficult to detect and monitor. This study introduces a novel, low-cost approach using computer vision to integrate time-lapse photos with lidar data to estimate water levels and flood extents. Applied to two case study flood events and validated against a two-dimensional flood model, this method shows how community-centered, adaptable monitoring systems can capture spatiotemporal flood dynamics often missed by traditional methods.

Soil moisture sensors often exhibit misleading daytime peaks because they are sensitive to temperature. This study proposes a method to correct the spurious diurnal cycle of surface soil moisture, using Fourier analysis with land reanalyses. The diurnally adjusted time series better captures realistic soil moisture behavior and provides more reliable insight into land–atmosphere interactions on a diurnal timescale.