In northwestern Italy, overall drought conditions appear to have worsened over the last 60 years due to both precipitation deficits and increased evapotranspiration caused by temperature increases. In addition to changes in drought conditions, changes in the characteristics of drought periods, both at a local and at a region-wide level, are found. Links between all the aforementioned changes and terrain characteristics are highlighted, finding generally worse conditions in lower-lying areas.

Root-zone water storage capacity (S_r) helps plants survive droughts and influences water and climate systems. Using GRACE (Gravity Recovery and Climate Experiment) satellite data, we estimated S_r globally and found that it exceeds 2 m soil storage in nearly half of the vegetated areas, far more than previously thought. Incorporating our S_r estimates into a global hydrological model improves evapotranspiration simulations, particularly during droughts, highlighting the value of our approach for advancing water resource and ecosystem modeling.

This study investigates stormflow patterns in a forested watershed in north China, highlighting the fact that delayed stormflow is governed by soil water content (SWC) and groundwater level (GWL). When SWC exceeds its storage capacity, excess water infiltrates, recharging groundwater and gradually elevating GWL. Rising GWL enhances subsurface connectivity and lateral flow, synchronizing watershed responses and, in extreme cases, causing a delayed stormflow peak to merge with the direct stormflow peak.

Meltwater from rock glaciers, frozen landforms of debris and ice, has gained attention in dry mountain regions. We estimated how much ice melts in Murtèl rock glacier (Swiss Alps) based on belowground heat flow measurements and observations of the rising and falling ground-ice table. We found seasonal aggradation and melt of 150–300 mm w.e. (20 %–40 % of the snowpack). The ice (largely sourced from refrozen snowmelt) melts in hot summer periods, infiltrates, and recharges groundwater.

We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.

Floods, droughts, and heatwaves are increasing globally. This is often attributed to CO₂-driven climate change. However, at the global scale, CO₂-driven climate change neither reduces precipitation nor adequately explains droughts. Land-use change, particularly soil sealing, compaction, and drainage, is likely to be more significant for water losses by runoff leading to flooding and water scarcity and is therefore an important part of the solution to mitigate floods, droughts, and heatwaves.

This study investigated rainfall in eastern Australia to search for patterns that may aid in predicting flood and drought. The current popular consensus is that such cycles do not exist. We analysed 130 years of rainfall using a very modern technique for identifying cycles in complex signals. The results showed strong evidence of three clear cycles of 12.9, 20.4 and 29.1 years with a confidence of 99.99 %. When combined, they showed an 80 % alignment with years of extremely high and low rainfall.

The contribution of groundwater to groundwater-dependent ecosystems (GDEs) is declining as a result of an increase in groundwater abstractions and climate change. This may lead to loss of habitat and biodiversity. This proposed framework enables the mapping and understanding of the temporal and spatial dynamics of GDEs on a large scale. The next step is to assess the global impacts of climate change and water use on GDE extent and health.

We compared hourly and daily extreme precipitation across Norway from HARMONIE Climate models at convection-permitting 3 km (HCLIM3) and 12 km (HCLIM12) resolutions. HCLIM3 more accurately captures the extremes in most regions and seasons (except in summer). Its advantages are more pronounced for hourly extremes than for daily extremes. The results highlight the value of convection-permitting models in improving extreme-precipitation predictions and in helping the local society brace for extreme weather.

Land changes and landscape features critically impact water systems. Studying two watersheds in China’s Greater Bay Area, we found slope strongly influences water processes in mountainous areas. However, this relationship is weak in the lower regions of steeper watersheds. Urbanization leads to an increase in annual surface runoff, while flatter watersheds exhibit a buffering capacity against this effect. However, this buffering capacity diminishes with increasing annual rainfall intensity.

Earth scientists assume that evaporation depends on the gradient in water vapour concentration (Fick’s law), but this is only true in an isothermal system. Temperature gradients can impact evaporation via the Soret effect. Here we evaluate the relative magnitude of the Soret effect and find that it is at least 2 orders of magnitude smaller than classical concentration-dependent mass (“Fickian”) diffusion. This result justifies the standard practice of assuming evaporation follows Fick’s law.

Climate-driven shifts in vegetation phenology have a significant impact on hydrological processes. In this study, we integrated a process-based phenology module into the SWAT-Carbon model, which led to a substantial improvement in the simulation of vegetation dynamics and hydrological processes in the Jinsha River watershed. Our findings highlight the critical need to incorporate vegetation phenology into hydrological models to achieve a more accurate representation of ecohydrological processes.

When water infiltrates into a snowpack, it alters the thermal state of the system. This work presents a first-of-its-kind multi-phase heat transfer model for local thermal non-equilibrium scenarios of water infiltration into an existing snowpack, such as during rain-on-snow events. The model can be used to calculate the formation of ice layers, as well as partial melting of the snow. Hence, it can support hazard assessment for flash floods and snow avalanches.

Coastal urban areas are highly prone to flooding from rainfall, storm surge, and the combination of both. We improve a coastal model and use it to quantify flooding from Hurricane Ida in the Jamaica Bay watershed of New York City (NYC), creating a flood map and flooded area estimation. Experiments with shifted storm tracks and rainfall timing at high tide show that Ida, already the worst rainfall in NYC, could have been worse. This highlights the area's vulnerability and the need for thorough flood risk analysis.

Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.

Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite the fact that they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what they truly reflect in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and timescales for accurate drought characterization and monitoring.

This paper presents a method to solve a certain type of equation controlling the storage of water in hydrological models. This equation is often solved with complex numerical methods that may lead to slow runtimes. The method, called the Quadratic Solution of the Approximate Reservoir Equation (QuaSoARe), is both fast and applicable to any equation of this kind regardless of its complexity. The method reduces runtime by a factor of 10 to 50 depending on the model.