There is unequivocal evidence that climate change will impact the risk profile of dams, which are critical for water supply and flood mitigation. We project changes in the overtopping risk for 18 large dams in Australia in response to global warming. We consider the impacts of climate change on rainfall depth, rainfall temporal pattern, and rainfall losses. Under 4 °C of global warming, the risk of overtopping floods was 2.4–17 times that of historical conditions.

This study evaluates the extrapolation performance of long short-term memory (LSTM) networks in rainfall–runoff modeling, specifically under extreme precipitation conditions. The findings reveal that the LSTM cannot predict discharge values beyond a theoretical limit and that this limit is well below the extremity of its training data. This behavior results from the LSTM's gating structures rather than saturation of the cell states alone.

We studied how river water and groundwater mix beneath Tarnów, Poland, by tracking natural physicochemical “fingerprints” in water over a year. Our study shows that the Dunajec River is the main recharge source for the underground reservoir, supplying drinking water to nearly 270,000 people. We present a cost-effective, transferable workflow to trace groundwater origins and flow speed, which promotes sustainable water supply management and ensures faster responses to potential pollution.

This study explores how snow dynamics and hydropower reservoirs shape monthly runoff in the Yalong River basin, China. Using 15 years of data and an extended Budyko framework, we found that snow accumulation and melt dominate runoff in high-altitude areas, while reservoirs increasingly influence lower elevations. These factors reduce runoff seasonality at the basin outlet, emphasizing how climate change and human activity alter water availability in cold, mountainous regions.

River water temperature is a key factor for water quality. Under climate change, inland water temperatures have increased, putting pressure on aquatic life and reducing the potential for human use. Here, future river water temperatures for Switzerland are studied. Results show that, towards the end of the 21st century, average river water temperatures will likely increase by 3.1 ± 0.7 °C. This is likely to increases the thermal stress on sensitive aquatic species such as the brown trout.

This study presents a model integrating attention mechanisms and physical constraints to improve flood prediction. It forecasts floods up to 6 h in advance. The model enhances accuracy by focusing on critical input features and historical patterns. Results demonstrate its superior performance compared to other models, offering improved flood prediction with greater interpretability and alignment with physical laws.

Wetlands provide vital benefits like flood control, drought resilience, and carbon storage, but these depend on how water moves and is stored within the wetland. Using satellite imagery and AI, we mapped water patterns in 43 Swedish wetlands, identifying five hydrological types. This approach reveals differences in flooding and drying, linking wetland water behaviour to the benefits they provide, offering a practical way to understand wetland functions. 

Climate change in Central Europe results in the gradual replacement of spruce trees with beech. To model its potential impact, we used 22-year data of soil moisture under both tree species. The drier the summer season, the greater the difference between the two: the main reason was the higher transpiration of beech canopy compared to spruce. An accelerating transition of the Central European montane forest water balance from a fully energy-limited state towards water-limitation was documented.

This study presents a novel framework for accurately quantifying wetland depression water storage capacity. The framework and its concept are transferable to other wetland areas in the world where field measurements or high-resolution terrain data are unavailable. Moreover, the framework provides accurate distributions and depth–area relations of wetland depressions, which can be incorporated in wetland modules of hydrological models to improve the accuracy of flow and storage predictions.

Droughts in the Sahel are becoming more severe and unpredictable due to climate change. This study explores how large-scale climate patterns influence drought trends in the region. Using advanced data analysis and machine learning, we identified key climate factors driving droughts and mapped areas most at risk. Our findings provide a clearer understanding of regional drought dynamics, helping policymakers and communities develop effective strategies for water management and climate adaptation.

When floods happen during or shortly after droughts, the impacts of each of the events can be magnified. In hydrological research, defining these events represents a challenging and important task in the process of understanding where and why they occur. We have used real-word examples to address some of these challenges and show different approaches influence outcomes. We make suggestions on when to use which approach and outline some pitfalls of which researchers should be aware.

Extreme storms and droughts have profound impacts on water quality. We adopted a stochastic rainfall generator to test how rainfall changes affect the transformation and transport of nitrogen (N) and its potential effects on water quality. We found that wet/dry conditions and patterns within a year influence N transformation and transport. Mineralization and plant uptake are key processes governing the impact of nitrate on water quality under varying rainfall conditions.

Traditional hydrological models typically operate in a forward mode, simulating streamflow and other catchment fluxes based on precipitation input. In this study, we explored the possibility of reversing this process, inferring precipitation from streamflow data, to improve flood event modelling. We then used the generated precipitation series to run hydrological models, resulting in more accurate estimates of streamflow and soil moisture.

This study develops simple mathematical solutions to predict heat-driven chemical reactions in geothermal systems without relying on complex kinetic calculations. It examines how hot fluid injection into aquifers leads to mineral dissolution and precipitation, with implications for geothermal energy, groundwater resources, and geologic carbon storage. The findings highlight that natural processes often involve stationary reaction zones shaped by slow geologic processes.

Forecasts provide information crucial for managing floods and for water resource planning, but they often have errors. “Post-processing” reduces these errors but is usually only applied at river gauges, leaving areas without gauges uncorrected. We developed a new method that uses spatial information contained within the forecast to spread information about the errors from gauged locations to ungauged areas. Our results show that the method successfully makes river forecasts more accurate.

Estimating the sizes of flood events is critical for flood risk management and other activities. We used data from several sources in a statistical analysis of flood sizes for rivers in the Philippines. Flood size is mainly controlled by the size of the river catchment, along with the volume of rainfall. Other factors, such as land use, appear to play only minor roles in flood size. The results can be used to estimate flood size for any river in the country, alongside other local information.

We investigated what users want from the next-generation of hydrological monitoring systems to better support science and innovation. Through literature review and interviews with UK experts, we found that beyond providing high-quality data, users particularly value additional support for collecting their own data, sharing it with others, and building collaborations with other data users. Designing systems with these needs in mind can greatly boost long-term engagement, data coverage and impact.

Missing input data are one of the most common challenges when building deep learning hydrological models. We present and analyze different methods that can produce predictions when certain inputs are missing during training or inference. Our proposed strategies provide high accuracy while allowing for more flexible data handling and being robust to outages in operational scenarios.

The challenge with current satellite-based soil moisture products is their coarse resolution. Therefore, we used machine-learning model to improve spatial resolution of well-known SMAP (Soil Moisture Active Passive) soil moisture data, by using in situ soil moisture observations and additional weather data and vegetation properties. Comparisons against independent data set show that the model estimated soil moisture values have better agreement with in situ observations compared to other SMAP-related soil moisture data.

We present a neural network model that estimates river discharge based on gridded elevation, precipitation, and solar radiation. Some instances of our model produce more accurate forecasts than the European Flood Awareness System (EFAS) when simulating discharge with lead times of 50 days on the Neckar river network in Germany. It consists of multiple components that are designed to model distinct sub-processes. We show that this makes the model behave in a more physically realistic way.