This paper explores how streamflow reanalysis responds to precipitation forcing errors across 671 catchments in the United States. For the Global Flood Awareness System v4.0 driven by European Centre for Medium-Range Weather Forecasts Reanalysis v5, each 1 mm increase in precipitation error causes a mean increase of 0.51 mm streamflow error. This response varies by catchments as humid regions amplify the increased errors up to 2.5 mm, but arid and snowy regions dampen or delay them by storage.

This study explores how snowmelt and rainfall supply water to plants and groundwater in a high-elevation mountain grassland. Using field data and model simulations, we tracked how snowmelt and summer rain move through the soil. Snowmelt mainly supplies deep drainage, while rain mainly feeds plants. Nevertheless, during a very dry year, snowmelt contributed more than usual to plant water use. These results show how reduced snowfalls and droughts may affect the mountain water cycle.

his study explores how 20 years of European Space Agency Climate Change Initiative discharge products improve large-scale hydrological simulations through data assimilation. Tests in two contrasting models across Niger and Congo basins show that temporally dense discharge data reduces bias and improves internal variability. Although product quality, sampling, and basin processes control gain reliability, the findings support these datasets' value in refining models for climate studies.

River flow forecasts up to three months ahead can allow early preparations for future floods and droughts. We test a new forecasting system using weather forecasts made by selecting historical weather patterns that match current conditions and running them through a simulation of Great Britain's rivers. Our tests show that this system performs particularly well in the winter and spring, in northern Scotland and in southern England. We now use this system to produce forecasts regularly.

Hydrological models need to simulate both rainfall and snowmelt generated floods in regions with snow. We evaluated a deep learning model’s ability to capture timing and magnitude of floods generated by snowmelt and rainfall separately. Timing was better simulated for rainfall than snowmelt generated floods, whereas results for flood peak magnitudes were similar. Compared to an operational model, the deep learning model was better at simulating both flood types in the majority of the catchments.

While the effect of water bodies on flood peaks at different time resolutions has been demonstrated in the past, it remains unclear how they affect the ratio between daily and hourly peaks. Our results show that (1) hourly flows are dampened much more strongly than daily flows, which leads to similar daily and hourly flood peaks downstream of reservoirs; and (2) the attenuation effect is particularly pronounced in catchments that are heavily influenced by water bodies.

As the climate warms, drier air can change how water moves from land to the atmosphere. We used global satellite and climate data from 1981 to 2020 to study this change. We found that drier air was linked to greater water loss over about 61% of land, especially in warm and humid regions. Plant water loss was more strongly linked to air dryness than water loss from bare soil, while dry regions showed limits or declines. These results can improve forecasts of future water-cycle change.

We developed a new approach to improve the climate data used to study water and glaciers in high-mountain regions. By correcting unrealistic air temperatures and moisture while keeping the data physically consistent, we achieved much better agreement with measurements and more accurate simulations of snow, glacier change, and river flow. The method also preserved long-term warming trends, making it useful for studying future climate impacts in mountain areas.