Through a survey involving six water-dependent sectors, the relevance and impact of drought vulnerability factors for sectors and societies in forested cold climates were studied. Results show that the relevance and impact of vulnerability factors differ across sectors and how governance processes and policies are important for drought risk. Results offer unique insights into the dynamics of drought vulnerability that are valuable for risk assessment, drought plans, and increasing resilience.

This study examines the timing and topics of newspaper coverage of droughts in England. Media attention correlated with drought-prone hydroclimatic conditions, particularly low precipitation and low groundwater levels, but also showed a seasonality bias, with more coverage in spring and summer, as exemplified by the 2022 summer drought. The findings reveal complex media dynamics in science communication, suggesting potential gaps in how droughts are framed by scientists versus the media.

Accurate river flow measurements are essential for understanding river processes. This study evaluates the freely available software KLT-IV for automatic river surface velocity measurement. Analysing over 11 000 videos and comparing them with 274 traditional flow measurements, we find strong correlations (r² = 0.95–0.97) between KLT-IV and traditional methods. KLT-IV effectively estimates river flow with high accuracy, making it a valuable tool for autonomous water resource management.

This paper reformulates the model equations of the distributed Xinanjiang hydrological model in fully differential form and incorporates two-dimensional slope runoff routing methods, which are solved using appropriate numerical techniques. The main contribution is to provide an approach for distributed hydrological models that have evolved from lumped counterparts to reduce inherited numerical errors and to improve terrain representation, thereby enhancing their physical realism and simulation accuracy.

Our study examines how drought and extreme sea-level events affect saltwater intrusion in Thailand's Lower Chao Phraya River. We identified key drivers influencing this process and developed a numerical model to understand their effects and interactions. The model helps pinpoint effective ways to reduce saltwater intrusion impacts, providing practical guidance for managing freshwater resources sustainably in similar coastal areas facing changing climate conditions.

This study enhanced a popular water flow model by adding two components: one for snow melting and another for frozen ground cycles. Tested with satellite data and streamflow, the updated model improved accuracy, especially in winter. Frozen ground delays soil drainage, boosting spring runoff by 39 %–77 % and cutting evaporation by 85 %. These findings reveal that frozen ground drives seasonal water patterns. 

Our study focuses on filling in missing precipitation data using an advanced neural network model. Traditional methods for estimating missing climate information often struggle in large regions where data are scarce. Our solution, which incorporates recent advances in machine learning, captures the intricate patterns of precipitation over time, especially during extreme weather events. Our model shows good performance in reconstructing large regions of missing rainfall radar data.

Rating curves are used to estimate flow from recorded stage at hydrometric stations. Uncertainties in these flows are generally not estimated or reported. This paper proposed an approach to develop rating curves and to assess the corresponding uncertainties in estimated flow that can be readily applied to large-scale hydrometric networks. The mean standard deviations of estimated flows are of the order of 6.5 %. Uncertainties increase significantly for low stages and can reach values larger than 20 %.

This study uses a multi-model approach to assess future changes in river flow intermittency across France under climate change. Combining projections from the Explore2 project with historical flow observations, logistic regressions estimate the daily probability of flow intermittency (PFI) under RCP2.6, RCP4.5, and RCP8.5 scenarios. Results suggest intensifying and prolonged dry spells throughout the 21st century, with southern France more affected, while uncertainty remains higher in northern regions.

This study explores how streams react to rain and how water travels through the landscape to reach them, two processes rarely studied together. Using detailed data from two temperate areas, we show that streams respond to rain much faster than rainwater travels to them. Wetter conditions lead to stronger runoff by releasing older stored water, while heavy rainfall moves newer rainwater to streams faster. These findings offer new insights into how water moves through the environment.

Understanding and modeling flash-flood-prone areas remains challenging due to limited data and scale-relevant hydrological theory. While machine learning shows promise, its integration with process-based models is difficult. We present an approach incorporating machine learning into a high-resolution hydrological model to correct internal fluxes and transfer parameters between watersheds. Results show improved accuracy, advancing the development of learnable and interpretable process-based models.

The Aral Sea is both an example of large-scale environmental degradation caused by human activity and a message of hope through its partial restoration. Our study shows that the restored part of the Aral Sea is now healthy in terms of vertical mixing and oxygenation. However, small perturbations of water level or transparency could significantly alter the entire ecosystem. The results contribute to understanding the consequences of large-scale lake management worldwide.

This research examines how future climate changes impact root zone storage, a key hydrological model parameter. Root zone storage – the soil water accessible to plants – adapts to climate but is often kept constant in models. We estimated climate-adapted storage in six Austrian Alps catchments. While storage increased, streamflow projections showed minimal change, which suggests that dynamic root zone representation is less critical in humid regions but warrants further study in arid areas.

This study evaluates two reanalysis datasets, which are critical in climate, weather research, and water resources analysis, for the Australian region in terms of simulating daily mean precipitation and six other selected precipitation extremes. While spatial patterns of mean precipitation are well reproduced, substantial biases exist in precipitation variability, trends, and extremes. Caution in applying these datasets is thus advised in terms of the latter aspects.

This study presents a process-behavioural calibration approach for water balance models. The different calibration steps aim at calibrating different hydrological processes: evapotranspiration, the runoff partitioning into surface runoff, interflow, and groundwater recharge, as well as the groundwater behaviour. This allows for selection of a model parameterisation that correctly predicts the discharge at the catchment outlet and simultaneously correctly depicts the underlying hydrological processes.

To better utilize a given set of predictions, identifying “forecasts of opportunity” is valuable as this helps anticipate when prediction skill will be higher. This study shows that when strong land–atmosphere (L–A) coupling is detected 3–4 weeks into a forecast, the surface air temperature prediction skill at this lead time increases across the Midwest and northern Great Plains. Regions experiencing strong L–A coupling exhibit warm and dry anomalies, enhancing predictions of abnormally warm events.

Accurate groundwater level predictions are crucial for sustainable management. This study applies two machine learning models – Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) and the Temporal Fusion Transformer (TFT) – to forecast seasonal groundwater levels for 5288 wells across Germany. N-HiTS outperformed TFT, with both models performing well in diverse hydrogeological settings, particularly in lowlands with distinct seasonal dynamics.

As the soil thaws in summer, the land subsides, owing to the greater volume of ice than of water. This deformation helps monitor water storage because the subsidence magnitude is proportional to water volume. In this study, the  interferometric synthetic aperture radar (InSAR) technique was used to map subsidence around Toolik Lake, Arctic Alaska. Both InSAR and field observations suggest that soil water storage ranges from 0 to 75 cm, with small errors, and that the spatial distribution of soil water correlates strongly with topography and vegetation.

The review highlights the need to integrate urban land surface and hydrological models to better predict and manage compound climate events in cities. We find that inadequate representation of water surfaces, hydraulic systems and detailed building representations are key areas for improvement in future models. Coupled models show promise but face challenges at regional and neighbourhood scales. Interdisciplinary communication is crucial to enhance urban hydrometeorological simulations.

In the real hydrological Earth system, the relationships between variables are not constant. This study employed advanced statistical models, incorporating physical mechanisms to examine how evaporation stress responds to key factors over time. We found that stronger soil drought effects can be expected in areas undergoing rapid greening. This study may enhance the comprehension of evaporation stress, help optimize parameters in Earth system models, and promote greening initiatives.

We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, with clusters of 775 and 630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.

The study assesses the Global Precipitation Climatology Centre (GPCC) and Global Precipitation Climatology Project (GPCP) precipitation products by estimating hydrological drought recovery time (DRT) using satellite gravimetry data, Jet Propulsion Laboratory mass concentration solution (JPL mascon), and Global Gravity-based Groundwater Project (G3P) terrestrial water storage (TWS) products. The findings reveal that DRTs from GPCC and GPCP are comparable, and JPL mascon shows longer DRT, while G3P demonstrates greater consistency. These results contribute to a deeper understanding of precipitation and water storage dynamics and are essential for meteorological and hydrological research.

It is challenging to investigate flood variabilities and their formation mechanisms from massive event samples. This study explores spatiotemporal variabilities of 1446 flood events using hierarchical and partitional clustering methods. Control mechanisms of meteorological and physio-geographical factors are explored for individual flood event classes using constrained rank analysis. This gives insights into comprehensive changes in flood events and aids in flood prediction and control.

Impact Webs are a conceptual risk modelling tool designed to better understand and characterise complex risks. Understanding the complexity of risks is a key step in reducing and managing disaster risks. This paper outlines the rationale for Impact Webs and the steps for their co-creation and use. Impact Webs allow for an in-depth analysis while accounting for interactions within the systems in which they exist. They can be used to provide guidance for risk management options.

Understanding hydrological variability is essential for ecological conservation and sustainable development. Evapotranspiration influences the carbon cycle, and finding what causes its variability is important for ecosystems. This study shows that ENSO (El Niño–Southern Oscillation) influences not only South America’s rainfall, soil moisture, radiation, and evaporation but also other phenomena in the Atlantic Ocean. The impacts change regionally depending on the season analysed and have implications for heat extremes.

The unclear feedback loops of water supply–hydropower generation–environmental conservation (SHE) nexuses with inter-basin water diversion projects (IWDPs) increase the uncertainty in the rational scheduling of water resources for water receiving and water donation areas. To address the different impacts of IWDPs on dynamic SHE nexuses and explore synergies, a framework is proposed to identify these effects across the different temporal and spatial scales in a reservoir group.

The glacier-expanded SWAT (Soil Water Assessment Tool) version, SWAT-GL, was tested in four different catchments, highlighting the capabilities of the glacier routine. It was evaluated based on the representation of glacier mass balance, snow cover and glacier hypsometry. The glacier changes over a long timescale could be adequately represented, leading to promising potential future applications in glaciated and high mountain environments and significantly outperforming standard SWAT models.

Propagation from meteorological droughts to agricultural droughts under climate change is analysed in the monsoon-dominant Asian regions. Next, the persistence of such agricultural droughts across seasons in a water year is determined. Finally, the spatial concurrence of monsoonal agricultural droughts across regions has been estimated. South Asia and eastern China are projected to experience intensified future drought propagation and spatial concurrence.

Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models for simulating streamflow in river basins, redesigning them with the goal of integrating them in climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine-learning-based river models inside climate models.

Camera-based rainfall observation can measure rainfall with high spatiotemporal resolution and low cost. This study proposed a method for estimating rainfall intensity from images using the relationship between image information and rainfall intensity. The method was applied to outdoor cameras, and rainfall intensity could be estimated from the images. The method has the potential to facilitate the development of a camera-based rainfall observation technology that is accurate and versatile.

This study enhances irrigation modeling in the Common Land Model by capturing the full irrigation process, detailing water supplies from various sources, and enabling bidirectional coupling between water demand and supply. The proposed model accurately simulates irrigation water withdrawals, energy fluxes, river flow, and crop yields. It offers insights into irrigation-related climate impacts and water scarcity, contributing to sustainable water management and improved Earth system modeling.

Urban flooding can be driven by rain and storm surge or the combination of the two, which is called compound flooding. In this study, we analyzed hourly historical rain and surge data for New York City to provide a more detailed statistical analysis than prior studies of this topic. The analyses reveal that tropical cyclones (e.g., hurricanes) have potential for causing more extreme compound floods than other storms, while extratropical cyclones cause less extreme, more frequent compound events.

We introduce a novel method for improving daily precipitation maps in mountain regions and pilot it across three basins in the Hindu Kush Himalaya (HKH). The approach leverages climate model and weather station data, along with statistical or machine learning techniques. Our results show that this approach outperforms traditional methods, especially in remote ungauged areas, suggesting that it could be used to improve precipitation maps across much of the HKH, as well as other mountain regions.

Seasonal streamflow forecasts for snowmelt-dominated catchments often rely on snowpack data, which are not always available and are prone to errors. Our study evaluates near-real-time global snow estimates and climate oscillation indices for predictions in the data-scarce mountains of central Asia. We show that climate indices can improve prediction accuracy at longer lead times, help offset snow data uncertainty, and enhance predictions where streamflow depends on in-season climate variability.